Author : Christopher Choi (@christopher_c), Damon Lee (@i_naiveai)

Managing unstructured data (text, images, audio, video) for machine learning is labor-intensive, especially when labeling large datasets. A proposed solution is to use hierarchical structuring of labels – for example, having a broad category like “human” with sub-labels like “race”, “gender”, “age group”. The hypothesis is that a taxonomy of labels (categories and subcategories) can streamline annotation workflows and reduce labeling and preprocessing costs. In this report, we investigate existing research and industry practices to see if this hypothesis holds true. We examine how hierarchical taxonomies and multi-level schemas have been applied in data labeling pipelines, whether they improved efficiency or reduced resource use, and how this approach relates to modern data-centric AI techniques (like weak supervision or active learning). We also discuss how hierarchical labeling could integrate with emerging architectures such as the MCP(Model Context Protocol) for agent-based AI, and consider potential benefits for AI safety and fairness (through more consistent, transparent labeling).

A taxonomy in data labeling is a structured classification scheme: it defines categories and (optionally) subcategories in a tree-like hierarchy. In practice, a well-designed taxonomy can enhance labeling efficiency and consistency. By providing clear category definitions and relationships, taxonomy-based labeling gives annotators a structured guideline, reducing ambiguity. According to industry guides, a good taxonomy enhances efficiency and accuracy in data labeling, reduces training time, and improves data comprehension.

Hierarchical labels allow data points to be tagged at multiple levels of detail, capturing complex relationships and enabling algorithms to understand the label structure. For example, an image of a person might first be labeled as “Human”, and then at finer levels as “Human→Adult→Female”, etc. This structured approach contrasts with flat label schemes where every label is independent.

Why might hierarchy reduce cost? Hierarchical labeling focuses human effort where it’s most needed. Coarse labels can be applied broadly, and fine-grained labels are only added when relevant. This can prevent unnecessary annotation of irrelevant details. It can also facilitate reuse of past labels – for instance, once an item is labeled with a high-level class, those labels can bootstrap or constrain further labeling tasks. Hierarchies also enable breaking a complex labeling job into simpler stages, possibly handled by different annotators or automated tools, as we will see. Overall, taxonomy serves as a scaffold for efficient labeling, which many organizations and researchers have explored.

One clear example comes from Amazon SageMaker Ground Truth, a labeling service that explicitly supports hierarchical workflows. The AWS team demonstrated a “job chaining” approach: first label images with high-level classes, then feed those results into subsequent jobs for detailed labels. For instance, in an autonomous driving dataset, images were initially labeled as containing “vehicles”, “traffic signals”, or “pedestrians”. Only the images tagged as containing vehicles would proceed to a second task where annotators draw bounding boxes around each car. This filtered, multi-stage process avoids doing expensive annotation (like drawing boxes or segmentation) on images that don’t need it. AWS notes that “job chaining can be a cost-saving measure, if you only run more expensive tasks on images that have already been identified as containing the features that you want to bound”. They reported that combining such hierarchical task design with automated pre-labeling can reduce overall labeling costs by up to 70%. In practice, companies mix workforce tiers in these pipelines: e.g. using inexpensive crowd workers for simple broad classification, and expert annotators for fine labels on the subset that requires it. This tiered strategy underscores how hierarchical labeling yields efficiency by focusing human effort selectively.

Large-scale audio annotation has also embraced hierarchical structures. Google’s AudioSet is a notable case: it built an ontology of 632 audio event classes arranged in a hierarchy up to 6 levels deep. The motivation for a hierarchical ontology was explicitly to “aid human tagging by enabling a tagger to quickly and directly find the set of terms that best describe a sound”. In other words, rather than confronting annotators with hundreds of flat labels, the labels are organized (e.g. “Music → Instrumental → Piano” or “Animal → Bird → Chirp”), allowing a quicker navigation and decision process. This improves annotation speed and consistency. In AudioSet, some categories were even marked as “abstract” (not used as direct labels) to structure the ontology, and vague categories were blacklisted – this ensured labelers only use well-defined, discriminative tags, reducing confusion. The hierarchical scheme also supports multi-label tagging (each 10-second clip often gets multiple labels, e.g. a clip could be tagged “Speech” and “Dog bark” simultaneously). By structuring these labels, annotators on AudioSet achieved about 2.7 labels per clip efficiently, and the taxonomy helped maintain quality across the million+ clips. This case shows that for audio (and similarly for image/video), a carefully designed ontology can streamline the labeling process, reducing cognitive load and error rates for annotators.

In text domains, hierarchical labeling often appears in document classification. For example, web directories and news classification use taxonomies (e.g. Yahoo! Directory or the Open Directory Project) where articles might be categorized as “Science → Biology → Genetics” etc. Labeling such datasets by hand at scale is costly, so both academia and industry have turned to semi-automated approaches leveraging the hierarchy. A case study from Quizlet (an education tech company) illustrates this: Quizlet needed to label millions of user-created study sets with subject tags in an academic taxonomy (spanning 8 levels deep). Manually labeling every set was infeasible, so they adopted a machine-assisted labeling pipeline. They first built a broad taxonomy of subjects and then generated a large training dataset by programmatically assigning labels from that taxonomy using keyword heuristics (e.g. if a set’s title contains “mitochondria”, label it under Science → Biology). This yielded ~1 million noisily labeled examples which the team used to train a classifier, while only a small test set (~10k) was labeled manually for evaluation. The hierarchical structure of subjects was crucial: it ensured coverage of all topics and provided a way to verify that each predicted label fits into a known ontology of knowledge. This approach dramatically reduced hand-labeling needs while maintaining reasonable accuracy – an example of weak supervision with an ontology. In academia, a related success is the Trove system for clinical text: by leveraging medical ontologies (taxonomies of diseases, drugs, etc.) to create labeling functions, researchers automatically labeled electronic health records for entity classification. Remarkably, this ontology-driven weak supervision matched or exceeded the accuracy of a fully hand-labeled model, coming within ~2 F1 points of the supervised baseline. In other words, the ontology provided a hierarchical schema that the weak supervision engine used to assign labels, achieving high quality without exhaustively labeling data by hand. These examples underscore that hierarchical label schemas can be paired with automation (rules or models) to amplify human effort, tackling large text corpora with minimal direct annotation cost.

Video is a complex modality (often combining image frames, audio, and temporal events), and labeling video can be very costly. Hierarchical strategies here often involve decomposing the task. For instance, a video content moderation system might first classify a video at a coarse level (e.g. “documentary” vs “sports” vs “user-generated”), then apply specialized models or annotators to label fine-grained events or detect objects within the video if needed. In surveillance or activity recognition datasets, one might first label the scene type (indoor, outdoor, crowd, etc.), then only annotate person activities in those videos where persons are present. This concept mirrors the “filter then refine” approach seen in image labeling. A concrete example is the ActivityNet dataset (for human activities in video) which defined a taxonomy of activities (e.g. a top category “Sports” with sub-activities like “Playing basketball”, “Swimming”, etc.). Although annotating start/end times for activities in videos was manual, having the taxonomy allowed efficient divide-and-conquer: annotators familiar with a specific branch of the taxonomy could work on those videos, and automatic preprocessing could discard videos not relevant to certain branches. Similarly, the YouTube-8M video dataset used a large label space (over 4,000 entities) organized in a hierarchical namespace (e.g. “Animal→Mammal→Horse”), which helped in structuring the annotation process and in training hierarchical classifiers. While the literature on explicit cost reduction in video labeling via hierarchy is thinner than for images or text, the same principle applies – focus expensive annotations only where necessary. For example, one could use a pretrained image classifier to tag each video frame with broad categories, then only have humans review segments of interest. This layered approach ensures that the labeling effort (often measured in human-hours per video hour) is substantially reduced by not labeling everything from scratch in one pass.

There is substantial evidence that hierarchical labeling can improve efficiency and reduce costs in ML data pipelines. Key advantages observed include:

By structuring labels, one can avoid redundant work. As noted, Amazon saw significant savings by only performing detailed annotations on data flagged by earlier, cheaper steps. This can cut total annotation time dramatically – e.g., skipping fine labeling on 50% of data that doesn’t need it can nearly halve costs. Moreover, different tasks can be allocated to different annotator pools to minimize expense (cheap labor for broad labels, expensive experts for nuanced ones).

Existence labels function as a fundamental validation layer that significantly improves the efficiency of subsequent dataset validation processes. By first establishing whether an object or feature exists in the data (e.g., "contains human face"), these binary classifications create a foundation that can be leveraged across multiple downstream tasks. When AI systems or human validators confirm these existence labels, they effectively create a validated subset of data that becomes the starting point for more specific attribute labeling (such as facial recognition, emotion detection, etc.). This approach eliminates redundant validation efforts since items without the existence label can be automatically excluded from further specialized labeling workflows. The hierarchical nature of this process creates a validation pipeline where each level builds upon previously verified information, dramatically reducing the total validation workload required for new tasks and enabling more efficient resource allocation in both AI and human-powered validation systems.

Hierarchical approaches significantly enhance active learning efficiency. In multi-label classification scenarios, labels naturally organize from coarse to fine granularity, with varying costs and informational value. By strategically querying annotators about parent categories first and focusing on uncertain nodes, active learning algorithms optimize annotation resources.

Yan and Huang (2018) demonstrated this efficiency by prioritizing labeling at parent categories when doing so would clarify numerous descendant labels. Their experiments in document categorization and medical imaging revealed that hierarchy-aware querying substantially improved model performance with fewer annotations. This approach mirrors human cognition, learning broad distinctions before refining specifics, and implements a confidence-based decision system where uncertain branches trigger targeted human review.

To further mitigate error propagation risks, effective implementations incorporate independent verification paths for critical categories and periodic root-node validation sampling. These safeguards ensure hierarchical errors don't cascade throughout the system while maintaining efficiency gains.

Leveraging ontologies (hierarchical concept schemas) to drive weak supervision dramatically reduces labeling requirements. The Trove clinical text analysis project achieved a 10x reduction in labeled data needs through ontology-guided programmatic labeling with minimal rules. Similarly, Quizlet generated massive label sets using taxonomy guidance and simple regex patterns, effectively trading human effort for computational approaches guided by expert knowledge.

These successes demonstrate that existing domain hierarchies (medical ontologies, academic taxonomies) can generate large annotated datasets at a fraction of traditional costs. The hierarchical structure serves as a scaffold for generating labels while ensuring consistent schema adherence.

To address concerns about reifying problematic categories, effective implementations incorporate flexible hierarchy versioning and regular ethical reviews. This allows taxonomies to evolve while maintaining backward compatibility with existing labeled data and ensures categories remain appropriate and inclusive.

Hierarchical organization delivers benefits beyond direct labeling efficiency, significantly reducing data curation and management overhead. Taxonomy-tagged datasets enable effortless filtering and subsetting for specialized model training or targeted quality review of problematic categories.

Industry applications demonstrate these advantages - Amazon's hierarchical label manifests enable automatic creation of filtered subsets (e.g., "all images containing vehicles"), streamlining downstream processing. Structured labels serve as rich metadata that enhances data engineering efficiency while simultaneously improving annotation quality and consistency.

The hierarchical approach also enables structured annotator training, where labelers master broad categories before advancing to finer distinctions. This creates an accountability chain where errors can be traced to specific hierarchy levels, improving dataset reliability for safe AI deployment.The evidence strongly supports hierarchical labeling's effectiveness in reducing effort while enhancing quality. Industry adoption by major ML data pipelines and research support through active learning and weak supervision studies demonstrate its practical value.

The push toward data-centric AI (emphasizing data quality/efficiency over brute-forcing model complexity) resonates strongly with hierarchical labeling. Several data-centric techniques naturally complement this approach:

Data-centric workflows often use programmatic labeling to generate training data. Hierarchical taxonomies provide a framework to do this systematically. We saw that ontologies can serve as sources of weak labels. Another angle is semi-automated labeling with pre-trained models (e.g., using large foundation models to label data, then having humans correct or refine). If the label space is hierarchical, a foundation model could confidently assign high-level labels which humans then refine at a granular level. This human-in-the-loop process was noted by Kili Technology: foundation models can pre-label data, speeding up the process, and “you can then use a labeling copilot to do zero-shot labeling... to further label and refine”. A hierarchy guides the model’s labeling – for instance, the model might label an image as “Animal” and even guess “Dog” breed; even if the breed is wrong, a human now only needs to choose the correct dog breed rather than start from scratch. Such coarse-to-fine model assistance can drastically cut human workload.

When labels are structured, models can be trained in a multi-task fashion to predict broad and fine labels together, sharing representations. This often improves performance on low-data fine classes by transfer of knowledge from the coarse classes. From a data-centric view, this means one might get away with fewer labeled examples of rare subcategories if the model is concurrently learning the parent category which has more data. Hierarchical text classification research has shown that exploiting the parent–child label relationships can boost accuracy compared to treating all classes independently. Higher accuracy per label means less need to collect huge datasets for every single class, indirectly reducing labeling requirements. Thus, a hierarchy can amplify data efficiency by allowing generalization across levels.

As mentioned, active learning strategies can use hierarchies to decide an optimal “curriculum” of queries. This fits data-centric principles: spend labeling budget where it gives the most model improvement. For example, in a hierarchical emotion annotation task, an active learner might first ask annotators to label whether a tweet is positive vs negative (top level sentiment) for many tweets, and only later focus on nuanced emotion subcategories for those that are clearly positive or negative. The coarse labels quickly partition the data, and finer distinctions are pursued only where needed. This staged querying reduces wasted annotations on trivial or irrelevant distinctions.

In summary, hierarchical labeling aligns well with data-centric AI by making labeling more strategic. It introduces structure that algorithms can exploit, whether via weak supervision rules, transfer learning, or intelligent query selection. The result is more labeling bang for the buck: each human-generated label carries more information (due to relationships in the taxonomy), and fewer total labels are needed to train robust models.

Adopting a structured schema of labels can also have side benefits for AI safety, ethics, and fairness:

A common source of bias in ML is inconsistent labeling – different annotators may apply criteria differently, or unclear class definitions allow subjective judgments that skew the data. A hierarchical ontology usually comes with clear labeling guidelines (since you must define each category and its scope). This clarity improves consistency. As one best-practice guide states, “defin[e] label requirements upfront… guide all labeling teams in the same direction while minimizing bias… all labels are consistent... across different groups”. For sensitive attributes like race or gender, a taxonomy forces one to explicitly enumerate categories and criteria for each (e.g. definitions for each race category), which can reduce ambiguity and personal bias in labeling. Consistent labeling of demographic attributes could help mitigate biases in the model because the training data won’t reflect disparate labeling standards for different groups.

Hierarchical labels can make it easier to detect and correct bias in datasets. For example, if your data is labeled with attributes like age group, gender, or other facets under a “human” category, you can slice model performance by these attributes to check for disparate impact. If those attributes weren’t labeled (or were labeled haphazardly), such analysis would be harder. Moreover, a model trained to predict labels in a hierarchy can be more interpretable: one can see if the model’s errors concentrate in certain branches of the hierarchy. In content moderation, for instance, if a model misclassifies content, understanding whether it failed at a broad decision (say detecting violence vs non-violence) or a finer one (distinguishing specific hate speech targets) is valuable for safety – hierarchical labels provide that granularity. They also support more controlled model behavior; e.g., a hierarchical classifier could be designed to never skip the top-level check for “human present” before labeling race or gender, ensuring the model only applies those sensitive attribute predictions when context is appropriate (reducing false attribution of gender/race to non-human images, which has been a source of past AI bias incidents).

A thoughtfully constructed taxonomy can ensure that all relevant groups or concepts are represented, avoiding an oversight where one lump-sum label hides diversity. For example, instead of a single flat label “ethnicity” which might lead annotators to use their own judgment, a hierarchy could specify all the categories of interest. This encourages inclusion of minority groups in the annotation schema itself. It also allows for masking or abstaining at certain levels if needed for fairness – e.g., an application could choose to use the “human” label for detection but not use the finer demographic labels in model training to avoid sensitive attribute leakage, yet still have those labels available for bias auditing on the side. In this way, hierarchical labels provide flexibility to separate concerns (what the model is allowed to use vs what we track for fairness).

From a process perspective, hierarchical tasks can be used to train annotators and ensure quality. For instance, one could require that an annotator first achieves high accuracy on broad labels before they are allowed to annotate fine labels (since fine labels often require understanding the broad context). This hierarchical review structure is recommended to maintain label accuracy. A side effect is that it creates an accountability chain – errors can be traced to which layer they occurred, improving the overall reliability of the dataset which is critical for safe AI deployment.

In essence, hierarchical labeling contributes to safety and fairness by enforcing a systematic, transparent approach to annotation. It reduces random inconsistencies and makes the data labeling process (often a source of hidden biases) more inspectable and adjustable. That said, one must design the taxonomy carefully – an ill-considered hierarchy could itself encode biases (e.g. an over-simplified racial taxonomy). Thus, domain experts and ethicists should be involved in designing label hierarchies for sensitive applications to ensure they are appropriate and inclusive.

The Model Context Protocol (MCP) is a recent open standard that helps AI systems (especially AI assistants or agents) connect to external data and tools in a structured way. It essentially provides a uniform interface for an AI agent to fetch context or data it needs from various sources. Integrating hierarchical data labeling with MCP could further enhance AI agents in several ways:

An agent utilizing MCP can request data based on context. If data sources (documents, images, etc.) are labeled with a hierarchical schema, the agent can make very specific context queries. For example, an agent solving a task could use MCP to ask: “give me all images labeled Animal→Bird→Parrot from the database” if it needs to see parrots, or “search documents tagged Law→IntellectualProperty for keyword X”. The hierarchical labels act as an index that MCP can leverage to fetch relevant information quickly. This means less preprocessing for each query and more direct access to the needed subset of data. Essentially, hierarchical labels make the context addressable in a standardized way (like a path in a file system), which aligns well with MCP’s goal of providing a “USB-C”-like universal port for context.

In agent-based AI architectures, one often composes multiple specialized models or modules to handle complex tasks. Hierarchical labeling fits naturally into this. We can envision an agent that delegates subtasks along label hierarchy lines. For instance, an AI assistant agent might have an image analysis pipeline with one sub-agent that first does a general scene classification (broad labels), then hands off to another sub-agent for person attribute analysis if a person is detected. MCP could standardize how these sub-agents communicate intermediate results (passing along the “context” that entity_type=Human was identified, enabling the next agent to load the appropriate tools to classify race/gender/age). Because MCP is a two-way protocol, the agent could even request clarification from a human or another model at a specific node in the hierarchy if uncertain (e.g., “Is this object a vehicle?” before proceeding to identify its make/model). This is analogous to how active learning queries in a hierarchy work, but now happening dynamically in an agent’s reasoning loop.

MCP encourages standardizing how data is provided to models. If the training and evaluation data of models are annotated hierarchically, MCP servers could serve not just raw data but contextual slices or metadata in a consistent format. For example, an MCP server connected to an image database could allow queries like “get me all labels for this image” and return a structured JSON of hierarchical tags. This could be used by an AI agent to better interpret its environment or user inputs. When multiple agents or tools conform to the same taxonomy (via MCP), it reduces miscommunication. In effect, hierarchical labels become a part of the protocol contract – an agent knows that any data item will come with, say, a {labels: [ ... path ... ]} field containing its taxonomy path, which it can use to route the data to appropriate handling logic.

Many agent frameworks allow LLMs to invoke tools. If those tools are built around structured data (for example, a “classification tool” that expects hierarchical label options), then the agent (an LLM) benefits from knowing the taxonomy to interact properly. MCP could feed the taxonomy description to the agent as part of context, ensuring the agent’s prompts and instructions remain consistent with the label hierarchy. This synergy can prevent errors. Imagine an LLM agent tasked with labeling incoming content – if it has access to the official ontology via MCP (perhaps stored in a knowledge base), it can validate that it only assigns legitimate labels and perhaps break down the task (first decide top category, then subcategory, etc.), which is exactly how a human would approach it.

In summary, integrating hierarchical labeling with MCP and agentic systems can create a robust architecture for data understanding. It allows AI agents to more efficiently retrieve, process, and annotate data by using the taxonomy as a guide. While MCP is a new development (emerged in late 2024), we can foresee that as organizations adopt it, having a well-structured ontology of their data will be extremely beneficial. It’s an open area to explore how agents could even learn or refine taxonomies on the fly (e.g., an agent noticing new subcategories in data and updating the ontology), feeding that back into a data-centric improvement loop. This tight integration of structured data labeling with agent frameworks is largely novel – a promising direction for future research and system design.

Is the hypothesis already addressed? By and large, yes – the idea of using hierarchical structuring to reduce labeling effort has been independently arrived at in multiple domains. We saw concrete instances in industry (Amazon Ground Truth’s approach, Google’s AudioSet ontology, Quizlet’s auto-labeling pipeline) and supporting techniques in research (active learning algorithms, weak supervision via ontologies). These examples validate that hierarchical labeling can significantly cut costs and improve efficiency. The approach is an extension of intuitive strategies (coarse-to-fine analysis, divide and conquer, knowledge reuse) that have been part of machine learning and data management for years. So it’s fair to say the hypothesis stands on a foundation of existing work.

That said, there is novelty in explicitly unifying and extending this approach across different data types and new AI paradigms. Some white space for innovation includes:

Current approaches typically develop isolated taxonomies for different modalities (audio, text, images), limiting cross-modal analysis. Our framework bridges this gap while respecting each modality's unique characteristics.

Our solution maintains specialized taxonomies for each data type while creating meaningful connections through:

1. Abstract Concept Anchoring: Linking modalities via shared high-level concepts rather than forcing direct equivalence

2. Flexible Alignment: Using machine learning to establish connections between concepts, even when perfect matches don't exist

This approach reduces computational costs by computing cross-modal relationships only when needed. It provides practical integration while preserving the specialized techniques essential for each data type, offering a balanced solution to the challenge of unified multi-modal understanding.

Current practice typically uses a fixed taxonomy defined upfront (which, as Kili’s guide notes, is important to avoid relabeling everything mid-way). But in long-running AI systems, new categories emerge. How can we introduce hierarchy in a way that evolves over time without huge re-labeling costs? Some research could investigate active learning not just for data points, but for refining the taxonomy itself (e.g., suggesting when a new subcategory is warranted). An agent-based system might detect novel clusters in data and propose them as new branches. Handling an evolving hierarchy efficiently is an open challenge.

While it’s intuitive that hierarchical labeling saves effort, there is room for more quantitative studies on this. Future work might formally model the labeling cost as a function of taxonomy design (depth, branching factor, etc.) and empirically determine optimal strategies for different scenarios. For example, how many top-level categories should one use to minimize total time, or what is the trade-off between granularity of early labels and the accuracy gains later? Having such guidelines could make the approach more systematic.

Foundation models (large pre-trained models) are now part of many data pipelines. We can innovate in how hierarchical labeled data and foundation models interact. One idea: use a foundation model to perform hierarchy-aware data augmentation – generating synthetic examples for underrepresented subcategories by conditioning on parent category context. This blends data-centric augmentation with taxonomy knowledge. Another idea: prompting LLMs with taxonomy definitions to assist human labelers (like an AI coach ensuring consistency). There is initial work on using LLMs for labeling, but combining that with structured ontologies could further improve quality and reduce errors.

On the fairness front, there is white space in establishing best practices for bias-resistant taxonomies. While we argued taxonomy can mitigate bias, the design of the taxonomy itself can be tricky. Research could focus on methodologies to construct label hierarchies that are aware of potential biases (perhaps using bias metrics to decide label granularity for sensitive categories). Additionally, one could explore how hierarchical labeling might help with model robustness – e.g., if a model knows a hierarchy, can it fallback to a parent prediction when unsure about a child class (which might be safer than a wild guess)? This could prevent highly confident but wrong fine-grained predictions, a safety hazard in some applications.

Finally, since MCP and agent-based AI are cutting-edge, practically no published research yet covers how labeled data taxonomies could be leveraged in that context. This is an open field to invent architectural patterns where agents use hierarchical context to learn or to divide tasks. For example, designing an “annotation agent” that orchestrates a team of sub-agents (some vision, some audio, some NLP) to label a piece of multi-modal content using a shared ontology – all communicated via MCP – would be a novel system. It bridges data engineering and agent AI in a unique way.

In conclusion, introducing hierarchical structuring into data labeling pipelines is a well-substantiated idea that aligns with both human annotation workflows and machine learning efficiency needs. Existing work in academia and industry corroborates the benefits: taxonomy-based labeling improves consistency, accelerates the labeling process, and reduces the required human labor and cost, especially when combined with automation techniques like active learning and weak supervision. The approach has been successfully applied in domains ranging from image and audio classification to text categorization, demonstrating its generality across unstructured data types. At the same time, there are opportunities to innovate further – in refining how hierarchies are built and maintained, in merging this approach with the latest AI agents and protocols (like MCP), and in ensuring that the structured labeling paradigm also advances goals of fairness and transparency.

Ultimately, hierarchical labeling shifts some complexity from the labelers to the label schema itself. It requires upfront thought in designing the ontology and perhaps more coordination in the pipeline, but it pays off by simplifying each individual labeling decision and enabling smarter use of both human and machine intelligence in the labeling loop. This data-centric mindset – treat labeling as a structured, optimizable process – is increasingly crucial as datasets grow in size and scope. The hypothesis that hierarchical structuring can reduce labeling and preprocessing cost is not only supported by existing work, but is fast becoming a best practice for efficient AI development. Embracing and expanding this approach will be key to building scalable, cost-effective, and responsible AI systems in the future.

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