Whose Ground Truth? Embracing Ambiguity in Human-Centered AI

AI summary

Overview

Research area: Human-centered AI, machine learning data annotation, and AI governance — specifically the assumptions about "ground truth" that underlie how AI systems are built and evaluated.

Technical level: Intermediate. The paper is a position paper rather than an empirical study, so it requires no specialized mathematics, but it assumes familiarity with concepts such as annotation, aggregation, targets, and evaluation pipelines in machine learning.

Scope: The paper argues that AI development should stop collapsing divergent human judgments into a single ground truth and instead model the space of plausible human interpretations, while separating genuine ambiguity from annotation error.

What This Paper Is About

Most machine learning systems are built on the premise that a single correct answer exists for a given input. When human annotators disagree, standard practice either aggregates their labels into one consensus target or dismisses the disagreement as noise. The authors argue that for many human-centered tasks this premise is wrong: human interpretation is often genuinely ambiguous, and several different readings of the same input can be simultaneously reasonable and valid. The paper's goal is to make the case for treating that ambiguity as something to be modeled and preserved, rather than eliminated.

Key Contributions

1. Identifies the single-ground-truth assumption as a design choice with consequences. The paper frames the prevailing practice of resolving annotation variability through aggregation, or treating it as noise, as an assumption rather than a necessity, and argues it is a poor fit for human-centered tasks.

2. Draws a conceptual distinction between meaningful ambiguity and annotation noise. The authors insist that not all disagreement is equal — some reflects real diversity in human perception and judgment, and some is error — and that a workable approach must tell the two apart.

3. Proposes reframing the learning target as an interpretation space. Rather than predicting one definitive label, systems would aim to model the space of plausible human judgments for a given input.

4. Extends the argument across the full AI lifecycle. The paper calls for this perspective to shape how AI systems are represented, learned, evaluated, deployed, and governed, not just how training data is labeled.

Main Findings

• The dominant paradigm collapses disagreement. Conventional machine learning and AI systems are largely developed under the assumption that a single definitive ground truth exists, with variability in human annotations resolved through aggregation or dismissed as noise.

• Ambiguity is a property of the task, not a defect in the data. For many human-centered tasks, the authors argue, human interpretation is inherently ambiguous, and multiple interpretations of the same input may be simultaneously reasonable and valid.

• Forcing a single target discards real information. Reducing ambiguity to one target risks overlooking meaningful information about the diversity of human perception, judgment, and experience.

• The remedy is modeling, not averaging. The paper calls for a shift toward modeling the interpretation space of plausible human judgments, with an explicit separation between meaningful ambiguity and annotation noise.

• The implications span the lifecycle. The authors argue this perspective should guide representation, learning, evaluation, deployment, and governance — not just data collection.

• No empirical results are reported in the abstract. As a position paper, it presents an argument and a research agenda; the abstract contains no datasets, metrics, baselines, or experimental outcomes.

Methodology in Plain English

This is a position paper, so its method is argument rather than experiment. The authors survey the standard assumption embedded in how AI systems are built — that one correct answer exists per input — and analyze why that assumption breaks down for tasks involving human interpretation. They contrast that assumption with an alternative framing in which the goal is to capture the range of plausible human judgments, and they work through what that reframing would require at each stage of the AI lifecycle. The abstract describes no data collection, no user study, no model training, and no evaluation protocol; the contribution is conceptual and agenda-setting.

Why This Matters

Impact on research: If adopted, the argument redirects attention from improving aggregation techniques toward representing and evaluating distributions of human judgment. It raises a question that touches nearly every human-labeled dataset: what information was thrown away when annotator disagreement was averaged into a single label, and does that loss matter for the task at hand?

Real-world applications — the abstract does not enumerate specific applications; the following are domains its framing directly covers, since it concerns tasks where AI "interacts with people and makes decisions about them":

• Content moderation and annotation of subjective material, where reasonable reviewers disagree about categories such as toxicity or offensiveness.
• Medical or clinical decision support, where clinicians may legitimately differ in diagnosis or treatment judgment.
• Subjective assessment tasks such as grading, hiring evaluation, or quality rating, where human raters vary.
• Personalization or conversational systems that must respond to varied human interpretations rather than one canonical reading.

Industry relevance: Organizations deploying AI in any domain with subjective labeling decisions face this issue directly in their data pipelines. The paper suggests that a system optimized to reproduce a single aggregated label may be confidently wrong about large portions of a population whose interpretation differs from the majority or the annotator pool.

Future Directions

• Operationalizing the distinction between ambiguity and noise. The paper asserts these must be told apart, but does not specify criteria or procedures for doing so — a central open problem it leaves for future work.

• Concrete representations of the interpretation space. How to represent, store, and learn from a space of plausible judgments rather than a point target is stated as a direction, not resolved.

• Evaluation methods suited to ambiguous targets. If multiple judgments are valid, it is unclear what a correct prediction looks like and how systems should be scored; the paper calls for this to change but supplies no metric.

• Deployment and governance implications. The abstract states that deployment and governance should be guided by this perspective, but does not detail what policies, disclosures, or oversight mechanisms would follow.

Target Audience

Researchers and practitioners working on human-centered AI, data annotation and labeling methodology, and ML evaluation; product and data teams whose pipelines rely on aggregating human labels; and policy or governance specialists concerned with how AI systems represent and act on human judgment. Readers looking for empirical results, benchmarks, or implementation details will not find them here — the value lies in the framing of the problem and the research agenda it proposes.

Authors’ abstract

As AI systems increasingly interact with people and make decisions about them, understanding human interpretations becomes an important part of developing human-centered AI. Conventional machine learning and AI systems are largely developed under the assumption that a single definitive ground truth exists, with variability in human annotations often resolved through aggregation or treated as noise. However, for many human-centered tasks, human interpretation is inherently ambiguous, and multiple interpretations of the same input may be simultaneously reasonable and valid. Reducing such ambiguity to a single target risks overlooking meaningful information about the diversity of human perception, judgment, and experience. In this position paper, we call for a shift towards modeling the interpretation space of plausible human judgments, while distinguishing meaningful ambiguity from annotation noise. We argue that this perspective should guide how AI systems are represented, learned, evaluated, deployed, and governed, supporting more human-centered AI that better reflects the diversity of human interpretation.