Context and Symmetry in Auditing: A Case Study of Skeleton Inference in Motion Capture

2026-08-10

AI Summary

This work addresses the lack of effective auditing mechanisms in current AI-based motion capture systems, which hinders verification of whether inferred skeletal poses conform to authentic human behavior. To tackle this challenge, the authors propose a novel auditing framework that integrates contextual reasoning with biomechanical symmetry principles. By embedding practical application contexts into the evaluation process and combining motion capture outputs with empirically verifiable real-world measurements—even in the absence of ground truth or amid contested annotations—the method enables rigorous empirical auditing of system behavior. The approach successfully uncovers implicit assumptions and biases in how ground truth is defined within existing systems, thereby offering both theoretical foundations and practical pathways for trustworthy assessment of motion capture technologies.

Abstract

Humans are increasingly expected to interact with AI systems that observe and make inferences about them - but do these systems actually work? A standard approach to answering this question is AI auditing. Conducting an AI audit requires identifying how a system behaves (i.e., determining what types of inputs to audit it with and then observing and documenting actual system behavior) and contrasting that with how a system should behave (i.e., determining what the nominal outputs of a system should look like). We argue that this is best done through a contextual audit, which we introduce as a method for auditing measurements within the context of the practices that produce them. We show how contextual auditing enables the interrogation of assumptions implicit in the audit process and allows auditors to be explicit about what serves as ground truth, which we define as verifiable measurements about the real world against which systems are evaluated. We outline how the concept of symmetry from science and technology studies can enable audits when ground truth is unknown, unknowable, or contested. Finally, to demonstrate how contextual and symmetric audits can be conducted in practice, we present a case study of skeleton inference in motion capture and propose areas suggested by our case study as particularly fruitful for future audits of motion capture systems.

Problem

• AI auditing
• ground truth
• contextual audit
• symmetry
• motion capture

Innovation

• contextual audit
• symmetry
• ground truth
• AI auditing
• skeleton inference