Measuring AI Accountability Through Argumentation Analysis: Can Model Reasoning Withstand Scrutiny?

Current methods for evaluating AI accountability face challenges due to contested definitions of 'appropriate AI behavior' and inherent ambiguity, particularly in assessing the moral reasoning of large language models (LLMs). A novel approach proposes measuring AI accountability through argumentation analysis, evaluating a model's ability to defend its verdicts against critical questioning using a structured dialectical protocol. This method aims to provide a robust evaluation standard that functions effectively despite ambiguities in what constitutes ideal AI behavior.

Why it matters

The development of robust and adaptable methods for AI accountability is critical as AI systems become more autonomous and integrated into decision-making processes. This research offers a pathway to establishing accountability in complex AI systems, such as large language models, by focusing on their reasoning and justification capabilities rather than relying solely on potentially ambiguous 'ground truth' validations.

Key insights

• Existing AI oversight methods often rely on 'ground truth' validation, which is problematic when defining 'appropriate AI behavior' is ambiguous or contested.
• The evaluation of moral reasoning in LLMs and debate-based oversight frequently bypasses realistic ambiguity.
• An alternative standard for AI accountability is proposed: structural quality of a model's defense for its verdicts.
• This standard uses a four-phase dialectical protocol, based on Walton's argumentation schemes and Govier's argument cogency criteria.
• The protocol is designed to be adaptable to various reasoning frameworks and extends beyond simple multiple-choice evaluations.
• It considers both the reasoning leading to a verdict and its subsequent post-hoc justification.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2609.05088