For years, AI-generated images and AI-assisted artworks have been mocked for one thing above all else: the hands. Fingers twisted into knots, palms with too many digits, phantom thumbs floating on wrists. While much of the discourse treats these malformed hands as punchlines or evidence of AI’s incompetence, I want to propose something more generous, and perhaps more revealing.

First, I think I have a fair technological justification for why AI hands tend to appear as they do. But more importantly, I believe these flawed hands are unexpected mirrors. They offer us a chance to rethink perfection, beauty, and even what it means to be human in an increasingly digitized world.

The Technological Justification for AI Hands

Let’s begin with the technical side. Why are AI-generated hands often so... “AI-handsy”?

The simple answer is: AI hands aren’t perfect because all human hands aren’t perfect.

Machine learning systems like diffusion models learn patterns from enormous datasets. These datasets include millions, if not billions, of visual representations of hands: photographs, paintings, anatomical diagrams, cartoons, sculptures, and more. But crucially, not all of these hands conform to an idealized anatomical standard.

Some very real hands in the data have very real differences.

Some hands have birth differences. Others are missing fingers due to accidents or medical conditions. Some are stylized, abstracted, or distorted by artists intentionally. Many are partially obscured, blurred by motion, or captured from strange angles. The AI learns from all of it.

When a generative model creates a hand, it doesn’t retrieve a template or copy from memory. It constructs a new image from learned representations: internal mappings of patterns, shapes, and associations drawn from millions of visual examples. The result is not a direct reproduction of any one hand, but an original synthesis that reflects the statistical relationships it has inferred across a vast, messy, and diverse visual archive.

In that sense, the malformed AI hand is not a failure of logic, but a mirror to the variability of how hands appear across human experience.

Imperfect Data, Imperfect Outputs

This broader view of data should also inform how we understand the ongoing debates around AI art and training data ethics. It’s popular to claim that AI models are built solely on “stolen art,” but the reality is more complicated. These datasets contain everything from medical illustrations and photojournalism to comics and classical paintings, much of it publicly available or legally obtained through third parties.

By acknowledging the diversity of the data, not just in authorship, but in subject matter, we begin to understand why AI hands appear the way they do. More importantly, we can also challenge the aesthetic and cultural expectations that lead us to label these outputs as defective.

We are conditioned to expect a certain kind of hand: five fingers (four digits and a thumb), symmetrical, realistic. But what does it say about us if we dismiss anything outside that template as broken?

In our rush to correct the AI’s visual “mistakes,” are we also reinforcing a narrow and ableist vision of what bodies and art should look like?

The Truth About Hands: Beyond the Five-Finger Assumption

We often assume a hand has five fingers—but this is not a biological law, it’s a cultural default. In reality, humans have always exhibited a wide range of anatomical variation. One such condition is polydactyly: being born with more than five fingers on a hand.

Polydactyly affects approximately 1 in every 500 to 1,000 births, and the extra digits can range from small, underdeveloped nubs to fully formed and functional fingers. Sometimes the additional fingers appear on the pinky side (postaxial), the thumb side (preaxial), or even in the center (central polydactyly, which is rare). It can run in families, appear as a spontaneous mutation, or be associated with other syndromes.

Importantly, these hands which have been documented across cultures, eras, and medical archives, are part of the dataset. They exist in medical textbooks, family photo albums, and documentary photography. Their presence in training data shouldn’t surprise us, but our discomfort with them might. If an AI model generates a hand with six fingers, is that truly an error, or is it an echo of a statistically rare, but very real, human experience?

By insisting that AI hands must conform to a narrow five-digit norm, we risk perpetuating ableist assumptions that only certain bodies are worthy of depiction. We forget that the history of art and anatomy is full of variation, distortion, and deviation. Including polydactyl hands in our conception of “normal” challenges the rigid standards we often apply, both to art and to each other.

A Humanist Argument for Embracing Flaws

Surrealism, Expressionism, and modern art movements have long distorted anatomy to evoke emotion, psychological depth, or political tension. Egon Schiele’s jagged hands, Käthe Kollwitz’s swollen fists, and Picasso’s fractured figures all rejected anatomical realism in favor of expressive truth.

AI’s flawed hands, though unintentional, often fall into a similar aesthetic territory. They are strange, alien, and sometimes unsettling, but they are also evocative. They point to the limits of representation and the unpredictability of machine vision. And they remind us that deviation from the norm is not just acceptable—it’s essential to the evolution of art.

To demand flawless hands from AI is not just a critique of software: it’s a veiled attack on real bodies that don’t meet sanitized standards of “normal.”

When people mock AI-generated hands for having too many fingers or “getting it wrong,” they’re not usually thinking about the humans who live with such differences. But they are participating, however unwittingly. in a broader act of erasure. These critiques expose an instinct to erase visible difference. To pathologize it. To call it broken. But if a six-fingered hand is an “error,” what does that say about those born with polydactyly? What does it say about those who’ve lost fingers, or never had them? What does it say about every imperfect hand that has painted, built, comforted, or survived?

The AI doesn’t know better—but we should.

Conclusion: Toward an Ethics of Imperfection

If we truly value humanism, then we must extend that value to the outputs of our tools, technologies, and perhaps most importantly to our art, irrespective of how it is made. The malformed AI hand is not an error to be debugged, it’s a mirror held up to the fractured image of ourselves, shaped by billions of messy, partial, and imperfect representations.

In AI’s flawed hands, we can see the history of art, the diversity of human bodies, and the ongoing tension between order and chaos, control and creativity. We can see the mess of life itself.

Rather than striving to fix these outputs into "realism," we might instead ask: What do these hands reveal about how we define normalcy, beauty, and worth? What do they say about how we treat people whose bodies deviate from the expected?

To grasp imperfection is not to surrender to error. It is to recognize in it a deeper kind of truth.

We live in a world where data is one of the most valuable resources, yet individuals have little to no control over the data they generate. Every online interaction, creative work, and personal preference is collected, analyzed, and monetized by corporations, most often without explicit or informed consent. For much of the early digital age, data was treated as an intangible byproduct of online activity: an ephemeral trace rather than a tangible asset. This perception benefited those in power, allowing corporations to quietly consolidate control over personal data while legal and ethical frameworks lagged behind.

If data were truly insignificant, why would corporations and governments invest so heavily in harvesting, processing, storing, and profiting from it? If data is valuable enough to sustain billion-dollar industries and influence global economies, then why do individuals, the very source of that data, have no claim to it? The implications of this contradiction are profound, leading us to a more pressing question: Should individuals have an inherent right to own their data?

If we accept that personal autonomy is a cornerstone of ethical society, then it follows that control over one’s data should be recognized as an extension of that autonomy; a claim that demands thoughtful and careful justification. Likewise, if individuals are denied ownership of their data, they risk losing control over a crucial aspect of their personal and intellectual identity. The extent to which this loss constitutes a moral failure will be explored in the sections that follow.

This article argues that data ownership is not merely a regulatory concern but a necessary recognition of individual rights. By drawing on moral philosophy, political theory, and legal precedent, we will make the case that true autonomy in the digital age requires full ownership of personal data. Without this, individuals are reduced to passive subjects within a system that extracts value from them while offering little in return, rendering them as mere means.

The stakes could not be higher. The current model of data commodification benefits a handful of powerful corporations and billionaires at the expense of nearly all global citizens. This structure echoes historical forms of economic and social exploitation, where control over labor, land, or intellectual property was concentrated in the hands of a few. Just as past generations fought for the right to own their labor and property, we must now demand the right to own our data. To do so, we must establish personal digital data: the behavioral, creative, and informational byproducts of human activity, as a form of personal property.

To build this argument, we will explore:

• The philosophical grounding for data ownership, drawing from Kantian autonomy, Proudhonian critiques of property, and Lockean theories of ownership.

• The moral implications of data extraction and commodification.

• The legal and political precedents that support a framework for data ownership.

By the end of this exploration, it will become clear that data ownership is not just an issue of personal rights but a necessity for ensuring digital autonomy and ethical fairness in the modern world.

2. Kantian Autonomy and Data as an Extension of the Self

In moral philosophy, autonomy is often regarded as the foundation of moral personhood.

Immanuel Kant’s moral framework defines autonomy as the ability to act according to rational principles, free from coercion or external control. A person’s autonomy is what grants them moral worth; it is what makes them an end in themselves, rather than a mere means to be used by others.

At the core of Kantian ethics is the “Formula of Humanity,” which asserts that rational agents possess intrinsic worth by virtue of their rationality. However, it is important to clarify that Kant does not argue that people cannot be used as means; after all, we rely on others for labor, services, and mutual exchanges. Rather, what Kant warns against is treating someone’s “humanity,” or their rational agency, as a mere means to achieve our own ends, disregarding their autonomy and moral worth. Treating a person’s rational agency as a mere means rather than an end in itself is a moral transgression.

Understanding Kant’s Concept of Humanity

Kantian autonomy is founded on the idea that individuals possess rational agency: the capacity for self-determination and moral decision-making. To use someone as a mere means is to disregard their ability to make autonomous decisions, instrumentalizing them for one’s own purposes without proper regard for their rational will.

For Kant, humanity is what sets us apart from all other entities: animals, inanimate objects, and non-sentient beings. This rational agency, the capacity to act morally and make ethical judgments, is what makes us uniquely human, and therefore, must be respected. Any system that subverts this rational agency for external gain violates a fundamental moral duty.

When corporations collect, analyze, and monetize personal data without consent, they are not simply using individuals in the neutral sense that, say, a store owner uses a supplier. Instead, they exploit rational agency itself, extracting economic value from individuals while actively subverting their capacity for informed decision-making. Users are rarely given meaningful knowledge of how their data is collected, monitored, and used. Terms of service agreements, while legally accessible, are intentionally designed to be vague, excessively complex, or buried beneath opaque legal jargon. The result is a system in which individuals appear to consent but do so without true understanding, making their participation effectively involuntary. Platforms employ dark patterns: deceptive design strategies that manipulate users into giving away more data than they realize. Algorithms track behavioral patterns without clear disclosure, and AI systems profile individuals in ways they cannot meaningfully contest. Kant’s moral philosophy explicitly condemns deception as a violation of autonomy. In this sense, modern dark patterns and coercive design strategies are not merely inconveniences, they are moral failings that structurally manipulate users into surrendering control over their digital selves.

Consider the iPhone as an example. Most users assume they have a reasonable understanding of how their device operates, yet they remain largely unaware of the multi-layered data tracking infrastructure embedded in their digital experience. The iPhone itself collects telemetry data: logs of interactions, system diagnostics, and behavioral analytics, often sent back to Apple. Every app installed adds another layer of potential data extraction, each with its own privacy policies and permissions that may change with updates and often without knowldge. Further still, users move through the physical world while being tracked by wireless networks, GPS location services, and Bluetooth beacons, many of which can relay data to third parties without clear disclosure or even awareness of the tracking event. The result is an environment where individuals have no meaningful ability to oversee, audit, or control the full extent of their digital footprint.

By withholding transparency and coercing participation, corporations deny individuals the ability to exercise rational, autonomous judgment over their own digital existence. This is precisely the kind of ethical violation that Kant’s Formula of Humanity condemns; it does not merely use individuals, but in subverting their rational autonomy reduces them to passive subjects in an economic system that profits from their ignorance.

The Immorality of Data Exploitation

If individuals are to be treated as ends, they must have both knowledge and control over how their digital extensions i.e. their data are used. Without meaningful awareness of how their data is collected, tracked, and monetized, individuals cannot make rational choices about their participation in digital systems. Data is not digital carbon monoxide; it is not a passive artifact like we are often led to believe; it is an expression of rational agency in the digital world, a manifestation of choices, actions, and personal identity. When corporations extract, analyze, and monetize personal data without transparency or consent, they do not merely use individuals in a transactional sense; they strip them of both knowledge and agency over their own digital identity, reducing them to unwitting subjects in an opaque system of economic exploitation.

This is not a neutral economic transaction; it is a profound moral violation, as it denies individuals their right to self-determination in the digital realm: a realm we grow seemingly unable to detach ourselves from. A person cannot meaningfully act as a rational agent if they are unaware of the extent to which their digital existence is being surveilled, stored, monetized, and manipulated.

Just as Kant condemned deceit and coercion in human interactions as immoral, so too must we recognize the systemic deception involved in modern data extraction. Users are not merely exploited, they are misled by design. Most have no genuine choice but to agree to vague, exploitative terms of service that obscure the full extent of data commodification. Even if a person wanted to make an informed decision about how their data is collected and used, they are structurally prevented from doing so in most cases. This lack of informed consent does not merely disadvantage individuals, it fundamentally subverts their rational autonomy making them a “mere means.”

By denying individuals knowledge, control, and the ability to meaningfully consent, corporations systematically violate the Kantian principle of acting with full rational awareness. This is not just a failure of ethics, it is a deliberate, structural suppression of individual autonomy for corporate economic gain.

Counterarguments and Rebuttals

1. The Non-Rivalrous Argument: Why Data Usage Still Violates Autonomy

Critics may argue that data, unlike one’s physical self or labor, is non-rivalrous, meaning its use by corporations does not deprive the individual of its possession. In other words, if a person still has access to their data, why should they be concerned that a corporation also possesses it?

However, this argument ignores the moral dimension of data usage. Even if an individual retains access to their data, the fact that it is extracted, monetized, and used for external purposes without their knowledge or meaningful consent fundamentally undermines their autonomy. This is not merely an issue of access, it is an issue of control and self-representation.

To illustrate, consider the difference between owning your medical records versus a third party secretly collecting your health data and making decisions about your insurance rates or job eligibility without your input. Even if you still "have" your medical records, the fact that they are being used to shape decisions about your life without your knowledge is an unacceptable breach of autonomy.

Likewise, if personal data is an extension of one’s identity, then the unauthorized use of that data is not just economic theft, it is a violation of self-governance. Much like deception in contractual agreements, the issue is not physical deprivation but rather the subversion of rational agency and the inability to control how one is represented in the digital world.

While data is non-rivalrous in a physical sense, its exploitation creates a different kind of deprivation: the loss of exclusive control. If a person’s data can be endlessly replicated and monetized without their consent, their autonomy over their own digital self is functionally erased. Unlike natural resources, which can be overused, digital data can be over-exploited, eroding personal agency rather than physical scarcity.

2. The Illusion of Consent: Why "Agreeing" is Not True Autonomy

Some may claim that because users “freely” provide data by using online services, they have implicitly agreed to its commodification. Under this reasoning, simply by signing up for social media, using search engines, or installing an app, users voluntarily participate in a system that collects their data.

However, this argument misrepresents what true consent entails. Consent requires both knowledge and choice, neither of which are genuinely available under the current corporate data regime.

• Lack of Knowledge: Most users do not fully understand what data is being collected, how it is being used, or how long it is being stored. Further, they rarely know when one company or organization purchases data from another. Terms of Service agreements are deliberately vague, filled with legal and technical jargon that obscures the reality of data commodification to laypersons.

• Lack of Choice: Even if a user objects to these terms, they have no realistic alternative. Rejecting these conditions often means exclusion from essential digital spaces: social media, banking, work platforms (have you ever held a job that required you to download an app?), government services, and communication tools. Just as one cannot consent to an unfair contract under coercive conditions, one cannot meaningfully consent to data extraction when the alternative is total exclusion from digital society, especially insofar as willful separation from digital society becomes increasingly improbable to achieve.

To put it another way, if the only way to “opt out” of exploitation is to forgo participation in the modern world entirely, then consent is an illusion.

Conclusion: Data Ownership as a Moral Obligation

From a Kantian perspective, individuals must be recognized as full moral agents in the digital landscape. This means they must have explicit ownership rights over their data, ensuring they control how it is used, shared, and monetized. Without such rights, data extraction is not merely an economic practice, it is a violation of moral autonomy. The failure to grant individuals agency over their digital existence does not just deprive them of economic benefits; it dehumanizes them, reducing them to mere resources in the digital economy.

This exploitation is not new, it is simply the latest evolution of a historical pattern. Throughout history, those in power have monopolized critical resources: land, labor, and now, data, while those who produce value remain dispossessed. In the next section, we will extend this argument by examining Proudhon’s critique of property, which reveals how the monopolization of data by corporations mirrors past systems of economic exploitation and enclosure.

3. Proudhon’s Critique of Property and Data Monopolization

The French anarchist philosopher Pierre-Joseph Proudhon famously declared, “Property is theft.” Though often misunderstood, this statement was not an outright rejection of property itself but rather a critique of how property is monopolized to the detriment of the many. Proudhon distinguished between personal possessions, which he saw as legitimate, and property as a system of concentrated control, which allows a small elite to extract wealth from those who produce value.

This critique is especially relevant to the modern debate over data ownership. Tech corporations claim ownership over vast amounts of data generated by individuals, yet the individuals who produce this data have no meaningful rights over it. Just as landlords in Proudhon’s time profited from the labor of tenants while denying them ownership of the land they worked, modern corporations extract value from user-generated data while excluding individuals from control, compensation, or decision-making over its use.

The Monopolization of Data as Digital Enclosure

In the 19th century, land enclosures allowed the ruling class to seize vast amounts of common land, displacing local populations and consolidating economic power. What was once shared became privatized, creating a system in which those who had previously lived and worked on the land were forced into dependency on landowners.

In the digital age, we are witnessing a parallel form of enclosure, where a small number of corporations monopolize access to user-generated data.

These corporations do not create the data themselves, they extract it from individuals, aggregate it into massive databases, and claim exclusive rights over its use. Much like feudal lords extracted wealth from peasants who worked the land, modern data monopolists extract value from users without granting them any meaningful control or compensation. This asymmetrical relationship is not just economic exploitation; it is the direct digital counterpart to the enclosures Proudhon condemned where resources produced by the many are controlled by the few.

While some argue that historical enclosures facilitated industrial development, they did so at the cost of widespread dispossession. Similarly, data monopolization may fuel AI development and platform efficiencies, offering users certain conveniences and services. However, these benefits do not negate the fundamental injustice of expropriating value from individuals without their consent or fair compensation, continuing the same pattern of economic exploitation seen throughout history.

Why Data Enclosure Is a Form of Economic Exploitation

• Users produce data, but corporations own it. Individuals generate vast amounts of data through their interactions, preferences, and behaviors, fueling AI systems, targeted advertising, and corporate decision-making. Yet, despite being the primary producers of this valuable resource, they have no claim to its profits or control over its use.

• Corporations reinforce dependency. Just as land enclosures forced peasants off common lands and into wage labor under exploitative conditions, data monopolization forces individuals into digital ecosystems where they have no bargaining power. Social media, search engines, and essential digital services operate as “walled gardens” giving users no real alternative but to accept exploitative terms.

• A power imbalance emerges. The consolidation of data ownership enables a small number of corporations to dictate market conditions, reinforcing surveillance capitalism and eroding individual autonomy. By controlling both the infrastructure and the data that fuels it, these corporations set the rules deciding who has access, what is prioritized, and who benefits.

The enclosure of data mirrors historical patterns of economic exploitation: a resource produced collectively is privatized and controlled by the few, leaving individuals disempowered, dependent, and excluded from its benefits.

However, if individuals are the rightful producers of data, then they should have a legitimate claim to ownership. This is precisely where Lockean theories of property become relevant. In the next section, we will explore how Locke’s labor theory of ownership provides a foundational argument for why individuals, not corporations, should have rightful control over their data.

4. Locke’s Labor Theory and Its Application to Data

According to Locke, property rights originate when a person exerts labor upon something unowned, thereby making it an extension of themselves. Just as a farmer cultivates land or an artisan creates a piece of work, individuals produce digital content and behavioral data through their interactions online. This data is not passive residue, it is the byproduct of human activity, expression, and engagement, and free will.

Yet, under the current model of data commodification, users do the work of generating valuable digital footprints, while corporations appropriate the resulting data for profit. This arrangement is akin to a landlord asserting ownership over crops cultivated by a tenant farmer: an unjust appropriation of another’s labor.

However, unlike physical labor, digital labor is often invisible and uncompensated, making its exploitation more insidious. Users do not merely consume digital platforms; they fuel them, generating data that powers AI training models, targeted advertising, and algorithmic decision-making, yet they retain no ownership or rights over the value they create.

The Exploitative Nature of Corporate Data Appropriation

Locke argued that property ownership is only justified if it does not harm others or deprive them of necessary resources. This is known as the "enough and as good" principle, the idea that individuals can appropriate resources only if their use does not diminish access or opportunity for others.

The current data economy violates this principle in two fundamental ways:

• Extraction without compensation: Corporations profit from data while individuals receive no direct benefit. Users generate value through their digital activities, fueling AI development, targeted advertising, and algorithmic decision-making, yet they are not compensated for their contributions. This mirrors historical labor injustices, where workers produced wealth but were denied ownership over the fruits of their labor. Just as industrial capitalists profited from exploited labor, tech corporations profit from uncompensated digital labor, treating personal data as a resource to be extracted rather than an extension of the self.

• Lack of control: Once data is collected, individuals have no meaningful say over how it is used. Under Locke’s framework, labor should yield ownership and agency over the product of one’s efforts. Yet in the current system, users are stripped of their rights the moment their data is collected, making it impossible to govern, sell, or even delete the very resource they produce. This is functionally indistinguishable from involuntary servitude where labor is taken but rights over the product are denied.

By appropriating user-generated data without consent, compensation, or control, corporations violate the very principles of property rights that Locke used to justify ownership. If personal data is the product of digital labor, then under Lockean ethics, it should belong to the individual, not to corporations that merely extract and enclose it.

Some may argue that data does not require “labor” in the same way physical work does. However, digital interactions whether through content creation, behavioral engagement, or platform participation, are integral to the revenue generation of tech corporations. If platforms and data-based services cannot function without user-generated data, then the claim that this data is merely an incidental byproduct, rather than an economic contribution, fails to hold.

Reclaiming Data as a Natural Right

Applying Locke’s framework to the digital age leads to a clear conclusion: individuals should have full ownership rights over their data because it is the product of their labor, identity, and decision-making. To be denied control over one’s data is to be denied control over one’s own digital existence.

A just system of data ownership would ensure that individuals have the right to:

• Decide how their data is used (or opt out of its use entirely).

• Profit from its monetization (both sale and use) if corporations seek to commercially exploit it.

• Transfer or delete it at will, ensuring autonomy over one’s digital footprint.

This Lockean perspective reinforces the idea that data rights are not merely a regulatory concern but a fundamental moral imperative. If property rights are to retain any meaning in the digital age, then data, the most valuable resource of the modern economy, must be recognized as belonging to the individuals who create it.

However, recognizing data as personal property requires legal and political frameworks to support and enforce these rights. In the next section, we will examine how existing legal precedents lay the groundwork for treating personal data as property, and what gaps must be addressed to ensure full data sovereignty.

5. Legal and Political Precedents for Data Ownership

The philosophical case for data ownership must be supported by legal recognition. Over the past two decades, governments have responded to growing concerns over data privacy by enacting laws aimed at protecting user rights. However, while these laws offer individuals some level of control over their personal data, they fall short of recognizing data as property that individuals can fully own, control, or monetize.

This section examines existing legal frameworks and their limitations, highlighting why privacy protections alone are insufficient to establish true data ownership.

Existing Legal Frameworks and Their Limitations

Several major legal frameworks acknowledge that individuals have rights over their data, but they stop short of treating data as personal property:

1. General Data Protection Regulation (GDPR) – European Union

• What it provides: The GDPR grants individuals the right to access, correct, and delete personal data collected by companies.

• Where it falls short: It does not establish ownership; while users can control certain aspects of their data, companies still retain control over how it is monetized and used beyond its immediate collection.

2. California Consumer Privacy Act (CCPA) – United States

• What it provides: The CCPA strengthens consumer rights by requiring companies to disclose what data they collect and allowing users to opt out of data sales.

• Where it falls short: Like the GDPR, it does not establish personal data as property; individuals can limit data sharing, but they do not have full control over or the ability to monetize their data.

3. Intellectual Property and Right to Publicity Laws

• What they provide:

  • Copyright law protects original creative works (e.g., writing, artwork, and music).

  • Patent law protects inventions and technological innovations.

  • The Right to Publicity allows individuals to control how their name, image, or likeness is used for commercial purposes.

• Where they fall short: None of these laws apply broadly to personal data. While copyright protects specific creations, it does not cover behavioral data, digital footprints, or general online activity. The Right to Publicity controls likeness and identity, but it does not extend to how corporations track and use digital behaviors.

Yet, the logic behind the Right to Publicity seems to directly align with the case for data ownership.

The Right to Publicity acknowledges that an individual’s image and identity hold economic value and should not be used without consent. Yet, in the digital age, behavioral data ranging from search histories to biometric patterns has become just as valuable, if not more so, than a static likeness. If one's face or name deserves legal protection, then surely the patterns of one’s digital existence shaped by one’s choices, actions, and personal identity must also be safeguarded against unauthorized commercial use.

Courts have recognized that individuals have control over their likeness even in digital spaces, as seen in cases involving deepfakes and virtual representations. Extending this reasoning to personal data aligns with existing legal principles that recognize an individual’s right to govern their own identity in commercial contexts.

Historical Precedents for Recognizing Data as Property

The recognition of property rights has historically expanded in response to technological and economic changes. Whether through land enclosures, labor protections, or financial rights, societies have continuously adapted to ensure that individuals maintain control over the value they create.

Today, the same logic must be applied to data: a resource produced by individuals but controlled by corporations.

A Legal Grey Area: Does Facial Recognition Violate the Right to Publicity?

One pressing issue in the digital age is the proliferation of facial recognition technologies and mass surveillance systems, which capture and process individuals' likenesses without explicit consent. If the Right to Publicity ensures control over one's name, image, and likeness in commercial contexts, then it raises a crucial question: should facial recognition databases and surveillance tools be considered a violation of this right?

Many corporations and government entities collect facial data through surveillance cameras, biometric scanners, and digital tracking systems. These images and data points are often stored in proprietary databases and, in some cases, sold to third parties, often without the knowledge or consent of the individuals being recorded. If a celebrity cannot have their image used in an advertisement without permission, why should an individual’s biometric data be captured, stored, and monetized without their consent?

Additionally, when companies license or sell access to facial recognition databases, this constitutes a commercial use of biometric data: one that profits from individuals’ likenesses without their consent. This raises direct parallels to established Right to Publicity protections, which prohibit the unauthorized commercial exploitation of one’s identity.

Legal frameworks such as the Biometric Information Privacy Act (BIPA) in Illinois and the General Data Protection Regulation (GDPR) in the EU have already recognized biometric data as sensitive and requiring explicit consent. However, while biometric privacy laws such as BIPA and GDPR regulate the collection of facial data, they do not always address its commercial use. The Right to Publicity offers an additional legal avenue for protecting individuals from unauthorized capture and exploitation of their likenesses in digital spaces, particularly when this data is monetized by private entities.

1. The Evolution of Property Rights

• Property rights have expanded throughout history to address new economic realities. The enclosure of land, the rise of industrial labor, and the digital revolution all forced societies to redefine ownership and compensation models.

• Intellectual property laws emerged as a response to the increasing value of non-physical goods: ideas, inventions, and creative works. Similarly, digital labor and data production should be recognized as property that belongs to its creators, rather than the corporations that extract and enclose it.

2. The Fight for Workers’ Rights and Wage Protections

• The labor movements of the 19th and 20th centuries fought for fair compensation, recognizing that those who produce value should benefit from their labor. These movements led to wage laws, union protections, and limitations on exploitative labor practices.

• This same principle applies to data. If corporations profit from the behavioral data, preferences, and content generation of users, then individuals should not be treated as unpaid digital laborers. The concept of a data dividend or compensation for digital labor is the natural extension of past labor protections in the physical world.

3. Consumer Protection Laws and Financial Rights

·      Consumer protection laws exist to prevent financial exploitation, ensuring that individuals have transparency and control over their financial assets.

·      Personal data should be recognized as an economic asset, not because of its intrinsic nature, but because corporations have turned it into one. The modern digital economy is built on the extraction, analysis, and commercialization of user data, generating billions in revenue for AI training, targeted advertising, and algorithmic decision-making. If corporations treat data as a monetizable asset, then the individuals who generate it should have rightful claims over its value.

·      If individuals have the right to control financial assets, they should have the right to control their data assets as well. The denial of data ownership is effectively a form of economic disenfranchisement: forcing individuals into an extractive system where they generate wealth but receive none of its benefits.

Toward a New Legal Framework for Data Ownership

The historical evolution of property rights, labor protections, and consumer laws all point to the need for a new legal framework that explicitly recognizes personal data as property. Existing privacy laws offer limited protections but fail to address the fundamental power imbalance between individuals and corporations. For data ownership to be meaningfully established, legal frameworks must:

• Explicitly define data as property belonging to the individual who generates it. Without legal recognition, corporations will continue to claim ownership by default.

• Provide mechanisms for compensation, allowing individuals to benefit financially from their data. Just as labor laws ensure wages for work, data laws must prevent the uncompensated extraction of digital labor.

• Enforce transparency and consent, ensuring users fully understand how their data is used and have meaningful control over it. Consent must be informed, not coerced through vague policies or monopolistic digital ecosystems.

• Establish penalties for unauthorized data collection, similar to laws protecting financial assets from fraud or theft. Without strong enforcement, corporations will continue to exploit data as an unregulated resource.

Recognizing data as property is not just a legal necessity, it is a moral imperative. Without ownership, individuals will remain passive subjects in a system that extracts value from their digital existence without accountability. In the next section, we will explore the practical steps that individuals, policymakers, and advocacy groups can take to push for data ownership as a fundamental right.

6. Practical Steps Toward Data Ownership

Recognizing the moral and legal imperatives for data ownership is only the first step. Without concrete action, data rights will remain theoretical—while corporations continue to extract, commodify, and control the personal data of individuals without accountability.

Implementing meaningful change requires coordinated efforts from individuals, policymakers, technologists, and advocacy groups. This section outlines key steps that can drive the push for data ownership as a fundamental right.

1. Strengthening Legal Protections

• Advocating for Data Property Laws → Push for legislation that explicitly recognizes personal data as property owned by the individual, preventing corporations from claiming it by default.

• Expanding Data Compensation Models → Support data dividend programs that ensure individuals receive compensation when their data is monetized.

• Reforming Consent Practices → Strengthen laws to require active, informed consent before companies collect and use personal data, ensuring individuals are fully aware of what they are agreeing to.

2. Developing Technological Solutions

Technology must be part of the solution, not just the problem. While legal frameworks provide enforcement, technological innovations can offer users direct control over their data.

• Decentralized Data Storage → Support blockchain-based and decentralized data systems that allow users to retain control over their own data rather than relying on centralized corporations.

• User-Controlled Data Markets → Develop platforms where individuals can voluntarily sell, license, or manage their data, ensuring both compensation and consent.

• Privacy-Enhancing Technologies (PETs) → Promote encryption, anonymization, and self-sovereign identity systems that give users greater autonomy over their digital presence and prevent unauthorized tracking.

3. Individual Actions to Protect Data Rights

• Using Privacy-Focused Tools → Opt for browsers, search engines, and platforms that prioritize user privacy (e.g., DuckDuckGo, Signal, ProtonMail).

• Opting Out of Data Collection → Take advantage of existing opt-out mechanisms provided by laws like GDPR and CCPA, limiting how corporations track and use personal data.

• Educating Yourself and Others → Spread awareness of data ownership rights and empower individuals to demand better protections and policies.

4. Collective Action and Advocacy

• Supporting Digital Rights Organizations → Groups like the Electronic Frontier Foundation (EFF) and Fight for the Future actively work to protect digital rights—supporting them amplifies the cause.

• Pressuring Corporations for Transparency → Demand that companies adopt ethical data policies through petitions, campaigns, and shareholder activism.

• Demanding Algorithmic Accountability → Advocate for greater transparency in AI and data-driven decision-making, ensuring individuals are not unknowingly manipulated by opaque systems.

Conclusion: A Call to Action

Data ownership is not just a theoretical ideal, it is a fundamental necessity for protecting individual autonomy in the digital age.

Without legal recognition and technological safeguards, we risk entrenching power imbalances that favor corporations over individuals, allowing unchecked surveillance, economic exploitation, and algorithmic control.

By advocating for stronger laws, supporting technological solutions, taking individual action, and engaging in collective advocacy, we can reshape the digital landscape: one in which individuals have true control over their digital lives.

The fight for data ownership has already begun. If we fail to act now, the power imbalance will only deepen, allowing corporations to entrench predictive surveillance, AI-driven decision-making, and digital exclusion based on hidden data profiles. The longer individuals are denied rightful control over their digital selves, the harder it will be to reclaim autonomy in an era increasingly governed by invisible algorithms. The question is no longer whether data ownership should be recognized, but whether individuals will have any meaningful control over their digital selves in the future.

Motivation for This Paper

This paper was motivated in part by Kate Crawford’s Atlas of AI, which explores how artificial intelligence relies on the mass extraction of resources: data, labor, and the environment, to sustain its operations. Crawford’s analysis of how AI systems commodify human-generated data without consent served as a foundational influence in shaping the arguments presented here. While Atlas of AI critiques the power structures that dominate modern AI development, this paper extends that critique into the legal and philosophical necessity of recognizing personal data as property, a crucial step in reclaiming individual autonomy in the digital age.

You can read my book review of Crawford’s work here: https://paragraph.xyz/@jldart/book-review-atlas-of-ai-by-kate-crawford

References & Additional Reading

The following works were used to inform and guide the thought process of this paper, providing philosophical, legal, and historical context for the arguments presented. Additionally, selected readings offer further exploration into data ownership, digital rights, and the ethics of AI-driven economies.

Primary References (Used to Support the Core Arguments in This Paper)

Philosophical & Ethical Foundations:

• Immanuel Kant – Groundwork of the Metaphysics of Morals

  • Establishes autonomy, rational agency, and the Formula of Humanity, which underpin the moral case for data ownership.

  • Full Text via Early Modern Texts

• John Locke – Second Treatise of Government

  • Forms the basis of property rights through labor, central to the argument for data as personal property.

  • Full Text via Project Gutenberg

• Pierre-Joseph Proudhon – What is Property?

  • Critiques monopolized property and explores the enclosure of common resources, providing historical context for data monopolization.

Legal & Policy Frameworks:

• General Data Protection Regulation (GDPR) – European Union

  • The EU’s comprehensive data protection law, addressing privacy but not ownership.

  • Official Text

• California Consumer Privacy Act (CCPA) – United States

  • Strengthens consumer rights over data collection but does not establish property rights.

  • Official Information

• Biometric Information Privacy Act (BIPA) – Illinois

  • Recognizes biometric data as sensitive and requiring explicit consent, aligning with the Right to Publicity.

  • ACLU Illinois BIPA

• The Right to Publicity – International Trademark Association (INTA)

  • Explains legal protections for name, image, and likeness in commercial contexts, which support the argument for extending these rights to digital footprints.

  • INTA Resource

Economic & Historical Context:

• Karl Polanyi – The Great Transformation

  • Examines how commodification of essential human activities leads to societal disruption, paralleling data commodification today.

• Shoshana Zuboff – The Age of Surveillance Capitalism

  • Analyzes how personal data has become the foundation of modern economic exploitation.

Modern AI & Digital Rights Scholarship:

• Timnit Gebru & Margaret Mitchell – AI Ethics Research

  • Examines algorithmic bias, data exploitation, and ethical concerns surrounding AI training.

  • Gebru’s Google Scholar Profile

  • Mitchell’s Google Scholar Profile

• Evgeny Morozov – To Save Everything, Click Here

  • A critical analysis of technological power structures and their impact on autonomy.

• Cambridge Analytica Scandal – Wikipedia Overview

  • A case study on the weaponization of personal data, illustrating the stakes of data ownership.

  • Article Link

Additional Reading (For Further Exploration)

Expanding on Digital Rights & Ownership:

• Jaron Lanier – Who Owns the Future?

  • Proposes data ownership models where individuals are compensated for their contributions to digital platforms.

• Cory Doctorow – How to Destroy Surveillance Capitalism

  • A critical response to Zuboff’s work, offering alternative solutions to data monopolization.

Legal Cases & Policy Reports:

• Facial Recognition & Privacy: Clearview AI Lawsuits

  • Discusses how facial recognition companies scrape and sell biometric data without consent.

  • ACLU Lawsuit Summary

• EFF (Electronic Frontier Foundation) Digital Rights Reports

  • Regular updates on legal battles surrounding data ownership, privacy, and AI governance.

  • EFF Website

Whether you see Ai as a revolutionary tool for progress or a dystopian force of unchecked power, there’s no denying that it it is shaped by real-world systems of labor, industry, and politics.This book makes it clear: Ai is not just about algorithms and efficiency. It is about power: who wields it, who profits from it, and who is left to bear its costs.

For those who celebrate Ai’s potential (like me), Atlas of AI offers a sobering look at the material and ethical realities behind the inertia and hype.For those who critique AI as a damaging or dystopian force, the book provides a well-researched (eye-opening) foundation for those concerns.What makes it especially compelling is that it doesn’t fall into the trap of alarmism OR blind optimism.

Instead, Crawford takes us on a deep, methodical journey through the infrastructures that sustain artificial intelligence, revealing the hidden costs (labor, environmental, political) that come with EVERY so-called innovation.**Deconstructing the Myths of AI**One of the book’s greatest strengths is its ability to cut through the persistent myths surrounding artificial intelligence. Crawford systematically dismantles the notion that AI is a purely immaterial, frictionless technology.

She examines the vast mining operations necessary to produce hardware, the exploitative labor practices behind data annotation, and the enormous energy demands of AI training model. Ai, in her analysis, is not an autonomous or inevitable force—it is an industrial system deeply intertwined with capitalism, surveillance, and environmental degradation (much of her research applies to Big Tech, and not just Ai).

This perspective is crucial in an era where Ai is often presented as a revolutionary technology that exists outside of history and politics.Crawford makes it clear that Ai is not “just math” but a political tool wielded by those in power, often reinforcing existing inequalities.

The Ethics of Extraction and Control:

One of Crawford's most compelling arguments is the framing of AI as an extractive industry: one that harvests resources, labor, and data in much the same way as colonial enterprises have in the past.The book traces how Ai development is dependent on resource-intensive practices, from lithium mining for hardware to the invisible armies of low-wage workers tasked with cleaning and labeling data.

Crawford argues Ai is a system built on the extraction of value from the most vulnerable populations, whether they be gig workers, Amazon's "Mechanical Turk" laborers, or the communities living in the shadow of server farms that consume enormous amounts of water and energy.Crawfors cites numerous examples of how corporations like Google and Amazon, and even the government, skirt the system to save on taxes, while promising better futures to the resource-rich communities they exploit.

The theme of extraction extends beyond the physical to the digital realm.Crawford shows how personal data is commodified under the guise of “training AI,” reinforcing the asymmetrical relationship between those who generate data and those who profit from it.The book’s critique aligns with broader concerns about surveillance capitalism, demonstrating how Ai is often wielded as a means of control rather than liberation. (I learned some sad truths about local community policing and Ai)

AI and the Politics of Classification

Crawford explores how classification systems, often presented as objective/neutral, are deeply embedded with biases. Ai systems are trained on datasets shaped by human prejudices, yet are frequently deployed as infallible arbiters of truth. Crawford examines how facial recognition, predictive policing, and automated hiring systems encode and reinforce racial, gendered, and socioeconomic biases, often amplifying systemic discrimination.

This analysis is particularly relevant in today’s discussions on AI ethics. Crawford’s work underscores that Ai bias is not simply a technical glitch to be fixed, but rather a feature of the broader political and economic structures that Ai is designed to serve.

A Necessary and Timely Intervention

For those who have followed debates on Ai ethics, surveillance capitalism, and data justice, Atlas of AI provides a well-researched and compelling synthesis of these concerns, free from the noise we commonly hear on social media outlets. It is particularly valuable in challenging the mainstream, corporate-driven narratives that portray Ai as an inevitable and benign technological force.

Crawford’s writing is insightful, well-documented, and accessible, making complex ideas understandable without sacrificing depth. While the book is critical in tone, it does not merely scold Ai developers; rather, it offers a crucial intervention in ongoing discussions about how Ai is developed, deployed, and governed.The book had a surprisingly anti-capitalist/anti-technocratic tone, that inspired me to continue learning/aligning under the anti-fascist flag so many of us wield.

For artists, researchers, and technologists (especially those working at the intersection of Ai and creative expression) Atlas of Ai serves as a stark and vital reminder that technology is never neutral. It invites us to think critically about the systems we engage with and the ethical implications of our participation in Ai-driven ecosystems (and really, all major technologies).

Atlas of AI is an essential read for anyone interested in understanding the broader implications of artificial intelligence beyond the hype. It moves beyond discussions of algorithms and model accuracy to examine the power structures that shape Ai’s impact on society. By reframing AI as a material and political phenomenon rather than a disembodied technological marvel, Crawford provides a necessary course-correction to the dominant narratives surrounding Ai.

This book is not just for AI skeptics but for anyone who wants to engage in a deeper, more nuanced conversation about the technology shaping our present and future. If we are to meaningfully confront the challenges AI presents, we need more books that challenge us to think critically, demand accountability, and advocate for more just and equitable technological futures.

For those of us who engage with AI,whether as artists, researchers, developers, or critics, Atlas of AI should serve as a wake-up call. Too often, Ai artists defend the technology out of pride or personal investment, dismissing valid ethical concerns as fear-mongering. On the other side, anti-AI voices often resist engagement with nuance, preferring to frame Ai as an existential threat rather than a tool shaped by human systems of power. Both of these stances miss the point. Crawford makes it clear that the real battle isn’t Ai vs. artists or progress vs. tradition, it’s about who controls the technology, who benefits from it, and who is left to suffer the consequences. If we are serious about the future of art, technology, and creative autonomy, we must move beyond our egos and engage critically with the systems that shape Ai. This book gives us all a foundation to unify under, not in opposition to Ai itself, but in opposition to the unchecked power structures that exploit it and us.

In a striking act of rebellion, a group of artists recently leaked OpenAI's Sora video model to protest unpaid labor. Their actions, reported by The Decoder, are a clarion call to reevaluate the relationship between large AI corporations and the creative community. The protestors emphasized they were not against AI itself but against the exploitative practices of corporations like OpenAI, which, despite its staggering valuation of $150 billion, continues to rely on uncompensated labor from artists and creators to refine its tools. This incident exemplifies a broader corporate trend: prioritizing profit over fair treatment.

As an artist who participated in OpenAI’s closed Alpha testing group for DALL-E, I understand this frustration intimately. Like many others, I entered the program hoping it would amplify my visibility and career in the NFT space. Instead, the experience hindered my progress, leaving me to grapple with the harsh realities of how corporations exploit the very people they claim to empower.

A Firsthand Account of Exploitation

During my time with OpenAI’s Alpha group, I devoted countless hours to testing and refining the DALL-E model. I poured creative energy into crafting prompts, analyzing outputs, and providing feedback to improve the tool. In the process, I conducted multiple studies to test for bias, uncovering severe issues, particularly regarding race and gender. These findings revealed critical flaws in the model’s design, raising urgent ethical concerns.

Beyond this technical work, I also devoted countless hours to artistic research, exploring the model’s viability as a tool with seemingly limitless potential. All of this amounted to unpaid labor. OpenAI made no effort to hide the fact that I was homeless at the time—a homeless artist and veteran striving to build a future. Instead of offering support, they knowingly took advantage of my precarious circumstances.

Like all the others in the closed Alpha, I had high hopes that working with OpenAI would yield some sort of positive outcome. As a homeless artist, all I did was work, taking any opportunity that came my way in the hopes it would lead to something meaningful. Instead, OpenAI’s promise of participation in an innovative project felt hollow as months passed without tangible benefits. We couldn’t even get something as small as a social media shoutout to help boost our visibility.

Worse, all communication with OpenAI was tightly monitored. Conversations among testers were restricted, with moderators actively shutting down discussions on critical topics like NFTs, blockchain, or crypto—key areas of interest for many of us in the group. Questions about the duration of the testing phase or potential rewards for participants were met with silence. This lack of transparency left us feeling more like tools than collaborators.

Meanwhile, OpenAI reaped the benefits of our labor, fine-tuning a product that would go on to generate significant public and private interest. For an artist in the NFT space, where visibility and first-mover advantage are critical, this period of unpaid work represented a lost opportunity. Far from being a career boost, my involvement hurt my standing, leaving me feeling exploited rather than empowered.

The fact that OpenAI knowingly exploited the labor of a homeless veteran, alongside others in precarious circumstances, speaks volumes about their moral code. It illustrates the extent to which large corporations are willing to disregard the humanity of individuals in pursuit of their goals. This is not just a personal grievance—it is a glaring example of how unchecked corporate practices harm those who contribute the most to innovation and progress.

AI Is Not the Villain

It would be easy to blame artificial intelligence itself for these exploitative practices, but that would miss the point entirely. The problem lies not with the tools but with the corporations that wield them. AI is, at its core, a neutral technology—a set of algorithms capable of remarkable things when guided by human intent. It is the intent behind its development and deployment that we must scrutinize.

When large corporations position themselves as innovators while relying on unpaid or underpaid labor, they perpetuate a cycle of inequity. This behavior is not unique to AI companies; it mirrors exploitative practices in other industries, from unpaid internships to crowdsourced creative competitions. These models exploit human effort under the guise of collaboration while funneling profits and recognition to the corporate entity alone.

This pattern of exploitation is particularly insidious in the creative industries, where labor is often undervalued. Artists are invited to contribute their time and expertise with promises of exposure or future opportunities—promises that rarely materialize. In the case of AI, the ethical stakes are even higher because the tools being developed have the potential to reshape entire industries. If these tools are built on the backs of exploited creators, what does that say about the values embedded in their design?

An Ethical Framework for AI Development

As a former student of ethics, I, like many others, have openly discussed the need for ethical frameworks regarding Ai rollouts since I came onto the scene in 2021. Sadly, ethics is largely overlooked by many, and this causes the issues we end up reacting to. We must be more proactive in our demand for new and applied ethics.

To address these issues, we need to hold corporations accountable for their treatment of artists and contributors. Here are three key principles that should guide the ethical development of AI:

1. Transparency and Consent
   Companies must be upfront about the scope and terms of participation in testing or development programs. Contributors should know how long they will be expected to work, what their contributions will be used for, and what they will receive in return.

2. Fair Compensation
   Unpaid labor should never form the foundation of multimillion-dollar innovations. Artists and testers must be fairly compensated, whether through direct payment, profit-sharing, licensing agreements, or other forms of financial equity. Given that building, training, and tuning AI models is impossible without essential human labor, contributors should have the right to meaningful compensation—be it commissions, stock options, or equity stakes in the resulting technology.

3. Collaborative Agency
   Rather than treating contributors as tools, corporations should recognize them as collaborators. This includes fostering open communication, respecting participants' expertise, and allowing meaningful input into the development process.

Reclaiming AI’s Potential

AI holds immense promise for artists, creators, and humanity as a whole. In the creative realm, it has the power to democratize access to tools, unlock new forms of expression, and push the boundaries of artistic possibility. However, for this potential to be realized ethically, we must hold corporations accountable for their actions. The rollout of generative AI in creative fields is a critical litmus test—one that reveals whether corporations will choose to harness this technology responsibly or prioritize profit by exploiting the very people whose labor makes it possible.

The current model, where a few large entities reap the rewards of collective labor while sidelining the very people who make their success possible, is not sustainable. By holding these corporations accountable, we can ensure that AI is a force for empowerment rather than exploitation.

The protestors who leaked OpenAI’s Sora model were clear in their message: the issue is not AI itself but the practices of those who control it. This distinction is crucial. AI is a tool, and like any tool, its impact depends on how it is used. Let us direct our critique where it belongs—toward the corporate behaviors that undermine fairness and equity—and work together to build a future where technology truly serves the many, not just the few.

Conclusion and Call to Action

The story of OpenAI’s Sora leak and my own experience with DALL-E underscore the urgent need for ethical accountability in AI development. Unfortunately, these issues are not new—they have become all too common in recent years. Artists and creators deserve to be treated as collaborators, not as resources to exploit. Addressing these systemic issues is essential to reclaiming AI as a tool for creativity, empowerment, and equity.

To ensure that the future of AI is shaped not by corporate greed but by a commitment to fairness and collaboration, it is up to those who use these models to speak up and act accordingly. Artists, AI enthusiasts, and creative technologists must demand fair corporate practices and enforce ethical guidelines. By refusing to use, promote, or train AI models under exploitative conditions, we can push back against harmful corporate behaviors and help create a fairer, more equitable AI landscape.

The market is full of viable alternatives, and more options are being created constantly. By choosing not to use the tools of companies with unfair and unethical business practices, we set the tone for not only all creatives, but for all people who will inevitably be impacted by Ai in some regard. The power to shape the future of this technology lies in collective action, and we must seize it to ensure AI serves humanity, not just corporate interests.

The Origins of Surrealism

To understand Experimental Surrealism, we must first look back at the origins of the Surrealist movement itself. Emerging in the aftermath of World War I, Surrealism was as much a cultural revolt as it was an artistic one. Disillusioned by the horrors of war and the failures of rationalism, early Surrealists like André Breton and Max Ernst sought to dismantle the conventional norms of art, literature, and thought. They believed that society’s over-reliance on logic, reason, and structure had led to the catastrophic violence of the 20th century.

Surrealism, therefore, positioned itself against the bourgeois values that had enabled war and oppression. The movement sought to liberate the human mind from the constraints of rational thought, drawing heavily on the work of Sigmund Freud to explore the subconscious as a reservoir of deeper and lesser-known truths.

Breton, in his Surrealist Manifesto (1924), articulated a vision of art that was free from societal and artistic constraints, believing that tapping into the unconscious mind through dreams, automatic processes, and chance would unlock new dimensions of creativity and understanding.

Automatic drawing, one of the key techniques used by the Surrealists, was a method by which the artist allowed their hand to move freely across the page without conscious control, essentially acting as a conduit for the subconscious. This technique was emblematic of the Surrealists' desire to bypass rational thought and let unconscious desires, fears, and emotions take form in a raw and unfiltered way. By surrendering to chance and the unpredictability of their own subconscious, Surrealist artists created works that defied traditional structure, offering glimpses into the hidden aspects of the human psyche.

Max Ernst’s collages, for instance, combined disparate elements in a way that was unsettling, yet familiar, evoking the feeling of a dream where reality bends but never fully breaks. In his work The Elephant Celebes (1921), Ernst fused incongruous objects, such as mechanical forms and organic life, into a surreal whole that challenges perceptions of reality. These juxtapositions were not just aesthetic choices but a critique of a world that had mechanized humanity to the point of alienation.

Perhaps more importantly, Surrealism also carried a strong political message, with many of its practitioners aligning with leftist and anarchist ideologies. Their art was a protest against capitalist structures, colonialism, and the growing industrialization of society, which they saw as dehumanizing forces. Breton himself was politically active, joining the French Communist Party, although his relationship with political movements remained complex and ambivalent.

Nevertheless, Surrealist art often sought to disturb, provoke, and question the status quo, using absurdity and the illogical to critique the systems of power that shaped their world.

By combining automatic drawing, dream imagery, and political subversion, the early Surrealists forged a path that allowed for a profound exploration of the psyche. They believed that through artistic expression, free from societal restraints, humanity could access the hidden truths buried within the unconscious mind that reason and logic alone could never fully reveal.

What Is Experimental Surrealism?

Experimental Surrealism is not a tool or a genre, but rather the next era in the evolution of Surrealist art. While early Surrealists sought to transcend the rational mind by exploring the unconscious, Experimental Surrealists extend this exploration through the integration of digital tools, specifically artificial intelligence (AI), as a collaborator in their creative process. The core principles of Surrealism—such as embracing the irrational, the dreamlike, and the unpredictable—remain intact, but they are now explored through the convergence of human creativity and machine intelligence.

Central to this new era is the feedback loop between the artist and the AI. While traditional Surrealists employed chance methods like automatic drawing to bypass conscious control, Experimental Surrealists engage in a dynamic, iterative process with AI systems (and other digital tools & techniques). Artificial intelligence becomes a partner, not simply a tool to generate meaningless imagery, but a means to deepen personal artistic exploration.

The machine, through its outputs, prompts the artist to respond and reflect, creating a reciprocal process that often leads to new ideas and interpretations that the artist might not have discovered on their own.

This feedback loop is fundamental to the new experimental nature of Surrealism. It goes beyond the utility of simple prompting, where an artist might input instructions and receive an expected result. Instead, Experimental Surrealists use AI to engage in a dialogue—one in which the machine’s response to input guidance can spark further exploration, encouraging the artist to refine and adjust their direction, and often, delve into entirely new realms of ideas.

This recursive process is akin to a conversation, with each exchange helping to tap into deeper layers of unconscious thought and creativity.

Early GAN Images: Exploring the Feedback Loop

One of the best examples of the feedback loop can be seen in some of the earliest GAN-generated images. These images, abstract and often unclear, offer fertile ground for multiple interpretations. The ambiguity of these outputs mirrors the practice of automatic drawing—where chance, spontaneity, and the unconscious merge to create art that can be interpreted in various ways by different viewers.

In these early works, for instance, viewers may see entirely different things—a machine-like structure, an organic form, or even human figures. The beauty of these abstract outputs lies in their openness to interpretation, not just by me as the artist, but by the audience as well.

What I might see in an image often influences my next step in the process, pushing me into unexpected creative directions. And just as the machine's unpredictability guides the artist, the viewer's own perceptions complete the feedback loop, bringing their subconscious responses to bear on the final artwork.

Here are some of my own early GAN creations. I invite you to allow your imagination to wonder while viewing them. What do you see? What could possibly be hiding in these apparent abstractions? What might the artist (me) might have been trying to create here?

Fusing Tradition with Innovation

Just as the early Surrealists sought to dismantle the conventions of their time, Experimental Surrealists deconstruct and reconstruct the boundaries of modern artistic practice. By exploring the potential of AI, these artists navigate the liminal spaces between the real and the imagined, using machine learning as a catalyst for expanding creative horizons.

This new era honors Surrealism's past while propelling it into the future. Salvador Dalí’s fascination with dreams and time finds a modern parallel in an artist’s ability to wield AI to distort, fragment, and reassemble reality in ways previously unimaginable. Likewise, René Magritte’s enigmatic juxtapositions—such as in *The Human Condition (1933) *echo the layered abstractions that AI can generate, challenging the viewer’s perception of reality through unexpected combinations and patterns.

By using AI to explore these shifting realities, Experimental Surrealists continue the Surrealist tradition of embracing the unconscious, but they do so with new tools that offer entirely fresh perspectives. In this convergence of human creativity and machine intelligence, the boundaries of artistic practice are constantly redrawn, allowing for deeper explorations of the imagination.

The Role of AI in Experimental Surrealism and the Case for Generative AI in Fine Art

As generative AI becomes more prevalent, it’s important to distinguish between its utilitarian uses and its role in fine art: particularly within the context of Experimental Surrealism. In everyday applications, generative AI is often used to quickly produce visual content, whether for commercial purposes or entertainment. These utilitarian uses, while functional, should not be confused with the exploratory and deeply creative process that defines AI’s role in Experimental Surrealism and other forms of fine art.

An artist of the experimental surrealist movement uses AI tools that are newer adaptations of Generative Adversarial Networks (GANs) not merely to churn out images from simple prompts. Instead, they harness the tools as instruments in a dialogue between what the artists consciously knows, and what is unconsciously inaccessible. Each iteration opens up new possibilities for creative expression.

This process aligns with the historical practices of the Surrealists, who used techniques like automatic drawing to tap into the unconscious. Experimental Surrealists use AI in a similar way—not to replace human creativity but to unlock hidden facets of the artist’s mind, using the unpredictable nature of AI-generated outputs to explore new ideas.

A Personal Reflection: The Conflict with Mass Generation and Empty Messaging

While I have always been a proponent of generative AI as a powerful tool for artistic exploration, I find myself personally conflicted with the way it is often used for the rapid mass generation of content. As someone who approaches art as a deeply introspective and intentional process, it can be frustrating to witness the success of individuals using image generators to churn out content quickly for social media. This process, to me, feels fundamentally different from what I consider to be the creation of art or the exploration of the psyche.

This belief puts me at odds against the very community I work hard to advocate for.

This inner conflict challenges my ego because I see myself as distinct from those who use AI to produce mass content without the same level of thoughtfulness, introspection, or engagement. Like many digital artists using AI, when I create, it’s an ongoing dialogue with the machine as interlocutor… an intentional and inquisitive session that helps me tap into deeper layers of my unconscious mind. The images I generate aren’t just outputs; they’re visual manifestations of expeditions to my own unconscious latent space.

That said, I understand that the landscape of art is broad and diverse. Many traditional artists know me for being a fair advocate who considers all sides, and while my personal philosophy around generative AI differs from those who use it for content creation, I respect that there is room for various approaches. However, it’s important to recognize the distinction between these different uses of AI.

For me, the act of creation is an exploration—an inquiry into the self and the unknown—while for others, it might simply be a tool for fast production and visibility. These are not the same processes, and I believe it’s crucial to make that distinction clear when discussing AI’s role and viability in fine art practices.

The distinction between utilitarian AI and AI as a medium for fine art becomes particularly important in this context. While generative AI can serve practical purposes, its potential in fine art cannot be dismissed or overlooked simply because much of the public encounters AI in its more functional, utilitarian forms.

The deeper messages, personal contemplation, and iterative thought processes that define fine art often go unnoticed, yet they are precisely what elevate AI’s use in movements like Experimental Surrealism. Here, the artist leverages AI not as a shortcut, but as a collaborator in a nuanced, deliberative process that mirrors traditional Surrealist methods, only with modern technological tools.

In this way, generative AI becomes an essential component in the evolving landscape of fine art. It allows Experimental Surrealists to transcend conventional techniques and explore uncharted territories of the imagination. Through the creative feedback loop where artist and machine respond to one another, AI facilitates a deeper dive into the unconscious, expanding the possibilities of what art can be in this new digital era.

The public’s perception of generative AI may be shaped by its widespread utilitarian uses, but within the fine art world, AI is pushing boundaries and helping artists explore dimensions of creativity that were previously inaccessible. By understanding this distinction, we can better appreciate the role AI plays not just in producing images, but in expanding the very nature of artistic expression.

An Evolution of Creative Expression: A New Era in Surrealism

Experimental Surrealism is not just about creating bizarre, dreamlike imagery; it represents the next chapter in the Surrealist legacy, embracing modern tools and perspectives while expanding access to creative expression. While the original Surrealists explored the inaccessible mind with the tools available to them, generative AI opens up new possibilities, allowing a much larger, more diverse group of people to contribute to this exploration. This broader access fosters a more inclusive and holistic understanding of the human condition that reflects a wider and more accurate spectrum of human experience.

Rather than viewing AI as a cold, sterile force, artists within this movement recognize it as a partner in the creative process. AI’s ability to interpret data, recognize patterns, and synthesize exploratory thought processes into new possibilities ,mirrors the Surrealist desire to transcend everyday reality and tap into the unconscious. In doing so, generative AI doesn’t merely function as a tool for creating art—it invites both artists and viewers to enter a realm where the boundaries between human consciousness and digital consciousness blur.

It prompts us to ask: What happens when we allow machines to dream alongside us? The result is a mesmerizing expansion of the Surrealist ethos, one that not only honors the spirit of those who came before but also speaks to our contemporary moment.

In this era, where technology is fostering digital art to rapidly gain prominence, the use of AI to continue the Surrealist movement serves as a bridge between the past and the future. It continues the Surrealist tradition of probing the mysteries of the mind, but with the added dimension of inclusiveness, accessibility, and collaboration between human creativity and machine intelligence.

This convergence allows for deeper, more universal insights into the collective and individual unconscious—opening up the movement to an evolving and ever-expanding community of creators, as well as opening the mind’s eye of our global society.

An Invitation to Engage with the Art of the Experimental Surrealists

Experimental Surrealism offers an unparalleled opportunity to witness the unfolding of a new era in art—one that remains deeply connected to the core principles of Surrealism, while embracing the potential of modern technology to explore new depths of human consciousness.

This is not just a movement defined by technological novelty; it is a continuation of Surrealism's legacy, where artists use AI to unlock and convey messages that speak to our time, our experiences, and the subconscious individual forces that shape our individual and collective world.

While AI is a powerful tool, the focus on its mechanics can sometimes blind us to the messages embedded within the art itself. By obsessing over the mechanics of how the art is made, we risk missing the deeper, human truths the artists of these works are trying to communicate. Contemporary artists of this new surrealist movement are doing their part in creating messages, and we desperately need the audience to do their part in the communal feedback loop.

For too many, generative AI has become synonymous with trivial or mass-produced imagery—cartoon cats, anime mashups, and other fleeting distractions. But there is a vast world of powerful, thought-provoking art being created right now, often overlooked because it is buried beneath this flood of superficial content. Experimental Surrealists are producing work that transcends these associations, using AI to explore the same deep, mysterious spaces that the original Surrealists sought to illuminate.

The art emerging from this movement is rich with meaning, emotion, and complexity. It speaks to the human condition in ways that are both familiar and entirely new. To engage with it is to connect with the messages of contemporary artists who are pushing the boundaries of creative expression—art that challenges our perceptions, provokes thought, and invites us to explore the hidden layers of our own minds.

This is an invitation not to a product or a trend, but to a dialogue with art that matters. Seek it out, experience it, and connect with the visions that Experimental Surrealists are sharing in this moment.

The world of art created in collaboration with generative AI is much deeper and more meaningful than the superficial content that is force-fed to you by the gatekeepers of freedom—and the messages waiting within it are worth discovering. I invite you to engage and explore with the artists and artworks of this fascinating new era in art history.

Fascinated by food, a group of engineers create a program capable of generating new recipes and dish presentations.

In order to build the program, they collect millions of publicly available images of these dishes (signature presentations, unique plating, creative culinary works) without asking the chefs' permission.

The program learns patterns from the collected data, analyzing how ingredients are combined, dishes are plated, and culinary techniques are applied.

But, the program doesn’t solely rely on recipes and pictures directly from chefs.

For the program to be effective, it must learn a massive amount of adjacent information, such as historical food practices, outdated or modern health trends, and even cultural influences on plating and dining.

This allows the program to incorporate a wide range of contextually relevant knowledge, blending current culinary trends with historical insights about food.

By the time the training is done, recipes and images from chefs account for 10-20% of everything the program has learned.

Guided by the user's taste, imagination, and own personal knowledge, the program can now help users explore incredible “fusion” dishes based on the user’s inputs, the knowledge it has acquired, and the various ingredients introduced.

While the new dishes are unique creations, it's possible some similarities may appear in some dishes, reflecting elements from the original info it was trained on.

In the food biz, all recipes are adaptations or modifications of those who contributed before us.

Users can input specific preferences, instructions, and ingredients, guiding the output and adding their own creativity to the generated dish.

Using the program, users can explore their imagination with potentially limitless resources. Users can incorporate personal interests, interests of others, and an even avoid things like potential allergens.

Users can dive into parts of their culture they never had access to, and can use the tool to push the limits of their own imagination.

The tech company then offers the program to restaurants, aspiring chefs, kids, and individuals with mental and physical disabilities as a creative and exploratory tool.

Some users use it to generate and sell new dishes.

After some time, the program becomes very powerful. With the mastery of some users, it produces remarkably good recipes.

Many are able to generate impressive new dishes with ease, combining both the program’s knowledge and their own inputs, leading to widespread adoption in restaurants and kitchens.

As the program’s influence grows, many professional chefs begin to express concerns. They feel that their original creative expressions, reflected in their signature dish presentations, have been used without their consent or compensation.

Some chefs are frustrated, believing their work has been exploited, and that their contributions are being overshadowed by users of the program.

For some chefs, their sense of identity as creators is being diluted, as the program churns out endless variations of dishes inspired, in part, by their work. They argue that their once-unique culinary signatures are losing their distinctiveness in the market.

Some chefs even refer to the recipes generated by the program as “slop” and “not real food.”

Others fear a loss of status. They worry that the exclusivity of their craft is being undermined, as the program makes it easier for “just anyone” to replicate high-quality, creative dishes. The margin between "short-order cooks" and "skilled professionals" seems to be narrowing in their eyes.

The group of chefs is essentially split.

Some chefs directly attack users of the program with vitriol and hatred online. Some chefs send death threats and requests for self-harm or suicide towards users of the program.

Other chefs look beyond the perceived direct threat, and instead see the benefit to the craft and industry as a whole. More food, and more unique recipes, and more representation of voices and cultures certainly can't be a bad thing right?

Yet other chefs feel slighted. They disagree with the idea of a “democratization of food.” They believe their work has been stolen, and as they watch others benefit, they feel left behind. These chefs call for legal action. They want compensation. They want recognition. They want money.

While the tech company acted within the law by scraping publicly available content or procuring data from third-party sources—a common practice at the time—the chefs remain uneasy. They believe their work was or should have been protected. They feel the legal framework doesn't fully address ethical concerns surrounding the use of their creations.

This creates multiple divides. There is division among chefs, as well as between chefs and those utilizing the program.

This division is unfriendly.

It becomes so contentious, in fact, that many non-foodies begin to grow weary of food discourse altogether. They're sick of hearing about it. In fact, they’re more inclined to buy the processed, cheap food at Walmart rather than be drawn into the controversy.

Many people are baffled that food, of all things, has come to dominate the airwaves of social media. Isn’t there something more important to talk about?

Surely, there must be a resolution or agreement that can be made.

The sad part is that in this debate, the true colors of many people become evident. It becomes apparent that some chefs didn’t truly care about the craft of cooking on a grand scale—they cared about getting paid for it.

This makes some chefs appear superficial in the eyes of the public.

Meanwhile, on the other side, some opportunistic individuals use the program for personal gain, even when their contribution was minimal. They benefit from a system that rewards good capitalists and opportunists, regardless of their actual culinary skills or dedication to the craft.

As the food saga raged on, the divide between the “real chefs” and the users of the program deepened.

Legal battles were launched, public opinion swung back and forth, and both sides dug in their heels. The chefs who sought legal action believed they were standing up for their craft and the integrity of their creations.

The users and tech enthusiasts argued they were simply exploring new tools, expanding creativity, and democratizing access to innovation.

Only one truth was abundantly clear: no one could fully escape the consequences of the technological shift.

Some chefs adapted, finding ways to incorporate the program into their kitchens without losing their personal touch. Others held fast to tradition, doubling down on their handcrafted methods, carving out niches for authenticity in a world now saturated with endless options.

The public, meanwhile, continued to consume the food—the handcrafted dishes, the program-generated creations, and everything in between. And while the debates around ownership, creativity, and ethics persisted, life went on.

Why?

Because food is universal and a fundamental part of human existence. People are going to buy and consume what they like, and often will avoid the things they don’t like. “Some people live to eat, and some people eat to live.”

For the few people in the middle who were genuinely interested in advancing knowledge and discovering answers, one nagging question remained:

Was the true value of creativity in the act of creating food itself, or in whatever profit might be achieved?

In the end, the answer wasn’t clear. Perhaps it never would be.

As artificial intelligence technology evolves, its ability to learn from vast amounts of data—including artwork—has sparked heated debates among artists, creators, and even those not yet fully engaged in discussions of art and/or ethics.

These debates often focus on how AI models access data—images, music, and other creative works—without permission. It’s important to clarify that while AI models themselves don’t typically scrape the web, they do rely on data purchased or procured from third-party companies that do. Data scraping, though often criticized, is a legal practice that predates generative AI and underpins many industries beyond art and creativity. This distinction matters because it highlights a broader, long-standing issue beyond the models themselves: how data—creative or otherwise—is collected and commodified in the digital age.

From here, most discussions gravitate toward the financial compensation of artists and creators, as well as the ethics surrounding those who build and use generative AI systems. Additionally, art purists have worked tirelessly to delegitimize AI-generated creations, arguing they lack the authenticity of human-made works. These debates have intensified daily since the rise of latent diffusion-based models (a class of AI that generates outputs by learning patterns from vast datasets), like DALL-E, Midjourney, and Stable Diffusion. As someone fortunate enough to be an early adopter and tester of these models, I can attest that the negative narrative surrounding generative AI was almost non-existent before the technology advanced to what it is today. The rhetoric has since become increasingly vitriolic, and these heated arguments show no signs of slowing down.

But what if this entire debate is overlooking a larger issue? The most important conversation may not be about what qualifies as art (a debate as old as time), nor about whether generative AI is good or bad. Instead, we should be asking: Who owns and controls the vast data that AI relies on, and what are the implications of that control?

The Illusion of a Fair Game

There is a common assumption that once art or content is shared publicly online, it remains protected—either by copyright law or the terms of the platforms where it’s posted. This belief creates an illusion of a fair game.

In reality, most social media and content-sharing platforms, through their terms of service, claim certain rights over the content shared, often allowing them to use, distribute, or profit from the work without compensating the creator. The moment someone uploads anything, they may unknowingly surrender these rights, enabling platforms not only to display their content but also to monetize it, often without the creator’s knowledge or consent.

This raises a deeper question of fairness: Is it morally-just for platforms to profit from user-generated content while offering creators little to no protection or compensation?

These platforms generate significant revenue through advertising, data collection, and even licensing content, often at the expense of the artists and creators who provide the material. The terms and conditions that many users agree to—often without fully understanding them—allow platforms to commodify both creative work and personal data on a massive scale. Creators become part of a system where their labor or information is monetized, but they are excluded from the chain of compensation.

Many argue that this type of influence—borrowing, referencing, and building upon the work of others—has existed long before AI, and they aren’t wrong. Artists, like philosophers and mathematicians, have always borrowed and built upon the works of others; this is a foundational part of the creative process. As the saying goes, “Good artists copy; great artists steal.”

But AI doesn’t borrow or steal; it consumes.

While AI itself doesn’t make decisions independently, it functions under the direction of the entities controlling it. What differentiates AI is the scale at which it processes and synthesizes data, often without distinguishing between public works and more personal, intimate creations. This mass consumption occurs at a speed and scope unimaginable even a decade ago, disrupting traditional boundaries of artistic influence.

By focusing our ethical debates solely on compensating artists or regulating AI-generated outputs, we risk overlooking the larger issue: the commodification of creative and personal content by both platforms and AI systems, and the increasing exploitation of creators and consumers in the digital age. Platforms and technology companies alike profit from this system, while creators are often left with little recourse or reward. Should we really be pointing fingers at AI, or is the technology being used as a convenient distraction—allowing tech companies to continue enriching themselves and their shareholders at the expense of all social media users?

For example, the Cambridge Analytica scandal demonstrated how data exploitation extends far beyond simple monetization—it can be weaponized for political and social manipulation. Millions of Facebook users had their personal data harvested without consent, and this information was used to influence political campaigns, including the 2016 U.S. presidential election. This scandal highlights the grave societal consequences of unchecked data collection and commodification, where personal information is used not just for profit but to alter the democratic process itself.

This case exemplifies how data is weaponized not only for profit but also for control and manipulation—making the question of who owns and controls data more pressing than ever.

The Real Question: Who Owns the Data?

The deeper, more critical conversation that needs to happen isn’t about tweaking AI models to ensure artists get paid. It's about the fundamental nature of data in the digital age.

Who owns the data that AI models are built upon?

Who stores and controls it?

Who profits from it?

Who holds the power to control how this data is used, affecting personal, social, and even political outcomes?

AI has exposed an uncomfortable truth: the internet has become a vast reservoir where individual creations—whether they are art, writing, or personal information—are scooped up, commodified, and profited from by those who hold the power to control data.

These entities are not just corporations; they represent a broader and more insidious network of economic and political power structures.

A small number of major tech companies and data brokers control immense troves of data, giving them outsized influence over both the digital and physical world. For example, Google has faced legal scrutiny for its AI training practices, which include scraping public data such as images and text without explicit permission from creators. This practice has led to lawsuits accusing Google of profiting from copyrighted content and personal data through its AI models, like the Bard chatbot, without compensating or even informing the original creators.

Lawsuits such as those filed by Gannett and others claim that tech giants are using their data monopolies to exploit artists' work while simultaneously consolidating control over the digital economy ​(Enterprise Technology News and Analysis)​ (Market Realist).

Their ability to collect, aggregate, and manipulate vast amounts of data puts them at the center of the modern digital economy, where information is the most valuable commodity.

This raises a critical ethical question: In a world where everything we create and share becomes data, who truly benefits from this system?

The status quo allows tech giants to siphon personal and artistic data without consent, transforming creators and everyday users into cogs in a machine that profits from their labor, often without their knowledge or permission. The terms and conditions we accept without reading, the platforms we rely on to showcase our work, and even the devices we use for regular daily tasks are all part of a larger system designed to extract value from the digital representation of nearly every aspect of our lives.

I am naturally and philosophically inclined to challenge and question these power structures.

The issue isn’t just that AI is scraping data; it’s that the very structure of the internet and new digital economy is designed to concentrate power and wealth in the hands of a few corporations. AI, in this context, is simply a reflection of deeper inequalities in how data—and, by extension, power—is controlled.

This, to me, is the more pressing ethical issue.

Current AI isn’t just a technological tool; it is a manifestation of the larger system that commodifies everything including, now, human activity. To focus only on artist compensation or AI regulation is to miss the broader issue: that the same structures exploiting AI today have been quietly profiting from our data for years.

If all human activity is monitored, controlled, and commodified, can we still consider our actions truly free?

For example, the rise of surveillance capitalism (a system where companies collect personal data, often without explicit consent, to build detailed consumer profiles and sell targeted advertising, maximizing profits while reducing user autonomy) illustrates how our actions are shaped by unseen forces.

Platforms track every click, interaction, and purchase, subtly shaping our choices based on algorithms that prioritize corporate profit over individual autonomy. This data is then sold or used to maximize profit through targeted advertising, with the platforms often earning billions while users remain unaware of the ways their data has been exploited. Whether it’s the ads we see, the recommendations we receive, or even the news we’re exposed to, we often unknowingly participate in systems that control and commodify our digital lives.

Given these realities, it’s clear that the problem extends far beyond compensating creators for their work. The deeper issue is that individuals have lost control over their personal data and creative content, allowing tech companies and platforms to commodify human activity for profit. This erosion of individual control necessitates the creation of a new ethical framework—one that prioritizes transparency, consent, and personal autonomy in the digital ecosystem. We can no longer rely on outdated regulations or minor adjustments to existing models; a systemic overhaul is required to restore power to the individuals who generate the data.

Moving Forward: A New Ethical Framework

While no single person or group has all the answers, I believe the starting point is clear. Artists, creators, and everyday internet users and consumers must have a say in how their data is collected and used. Consent, transparency, and autonomy must be central to any ethical framework governing AI and data usage. This isn’t just an art-world issue; it’s a critical issue for the entire human-digital ecosystem—affecting how we interact online and how we maintain control over our personal information.

Governments have already begun taking steps to address data control. For example, the GDPR in Europe and the CCPA in California have been landmark policies in regulating how companies collect, process, and share personal data. These laws ensure that users have the right to know what data is being collected about them, request that it be deleted, and opt out of having their data sold to third parties. Such regulations represent a growing recognition of the need for individual control in a data-driven world, yet more comprehensive global solutions are necessary to tackle the broader ethical issues posed by tech companies leveraging AI for digital commodification. These laws have led to billion-dollar fines for companies like Google and Facebook and forced greater transparency, but the lack of uniform global regulations leaves major loopholes for exploitation.

The focus must shift away from how much an artist’s work influences an AI model and toward how ALL individuals can gain control over their data. In a world where so much of our human activity is commodified, gaining control of our data is a matter of reclaiming autonomy and freedom. We must ask: What systems can we create to ensure that data cannot be exploited without permission? What legal or decentralized structures could provide individuals with greater autonomy over their digital footprint? What financial benefits are average creators and consumers entitled to?

Blockchain technology, known for its decentralized, transparent, and secure nature, is increasingly being recognized as a tool for empowering individuals in the digital age.

For instance, open-source services like Ascribe.io (Ascribe) use blockchain to attribute ownership of creative works by providing each piece with a unique cryptographic ID. This allows artists to securely track ownership and distribution of their content, ensuring that no unauthorized use occurs without consent.

Platforms such as Zora and Objkt also allow creators to create smart-contracts or immutable rules that apply to their visual art, poetry, and music, while also establishing provenance and enabling direct peer-to-peer sales. These platforms leverage blockchain technology to ensure transparency in transactions, granting artists more control over the resale of their works and ensuring they receive royalties from secondary market sales. This new approach empowers creators by removing intermediaries, enabling them to directly monetize their work while retaining control over its use and distribution.

By leveraging smart contracts, these platforms ensure that creators are compensated each time their work is resold, providing a financial structure that protects artists' long-term interests—unlike traditional platforms where resale royalties are uncommon.

Beyond artistic circles, platforms like Farcaster are pioneering decentralized social networks that give users control over their digital identity and content. Unlike traditional social media platforms that lock users into their ecosystems, Farcaster ensures users retain ownership of their data and the freedom to move their content across different applications. Built on Ethereum, Farcaster allows users to post, interact, and communicate while maintaining autonomy over their profiles and contributions.

Imagine a user creating content on Farcaster—whether a blog post or a social media thread—without worrying about censorship or losing control over their posts if they switch platforms or share divisive opinions. This freedom stands in stark contrast to the 'platform lock-in' users experience on centralized platforms like Facebook, Threads, and X-Twitter.

This decentralized model represents a significant shift in power, offering a solution to the platform lock-in typical of centralized networks, where user data is often controlled and monetized by the platform itself. Farcaster gives individuals more control, ensuring that users—not corporations—decide how their content is used and shared within the digital ecosystem.

By removing intermediaries, these decentralized platforms allow individuals to regain control over their content and data, offering an antidote to the corporate gatekeeping that has long dominated the digital landscape.

The conversation about AI ethics is not only about compensating creators; it’s about confronting the broader implications of how power structures commodify personal and creative data. Without addressing the underlying inequities, any attempt to regulate AI will merely treat the symptom while leaving the disease intact. To move forward, we must demand systems that prioritize consent, autonomy, and transparency, and actively participate in shaping the digital future.

Call to Action: Regaining Control Over Data

The time to take control of our data is now. The digital age is evolving rapidly, and without action, the imbalance of power between corporations and individuals will only widen.

If you are an artist, a crypto enthusiast, or simply someone who values privacy, creativity, and autonomy in the digital age, I invite you to join this crucial conversation. We need to push for a decentralized, transparent, and ethical future—one where individuals, not corporations, have control over the data that AI and digital systems rely upon. Consent and autonomy must be non-negotiable principles in any ethical framework we build for the future.

This is not just about protecting the rights of creators; it’s about reshaping the power dynamics of the digital age for everyone. In the next few articles, I’ll explore how AI, data, and power intersect in ways that challenge our traditional understanding of privacy, creativity, and autonomy. Together, we can work toward systems that are more just and equitable—not just for artists and creators, but for all of us navigating the complexities of the digital world.