AI's Role in Judgment: A Dialectical Inquiry for Internal Audit and Assurance Professionals

The Nuances of AI and Judgment

The article delves into a dialectical inquiry, examining the capacity of Artificial Intelligence to exercise judgment. It moves beyond simple classification or prediction, focusing on the more profound concept of judgment itself. The core argument presented is that judgment is a two-layered, socially instituted practice. The first layer, the deliberative layer, involves the synthesis of reasons, evidence, and context, where AI can genuinely contribute. The second layer, the propriety/ownership layer, concerns endorsing a verdict and being answerable for it, a responsibility that must remain with a human agent or institution.

AI as a Co-Author, Not the Principal

The synthesis reached after three rounds of dialectical reasoning posits that AI can be a "deputized co-author" of judgment. This means AI's reasons-responsive contributions are constitutive of the verdict, making it more than just an instrument. However, AI cannot "own" the verdict. The accountability and moral ownership of a decision, including the capacity to be called to account, revise under criticism, and bear responsibility for consequences, are inherently human or institutional roles. This distinction is vital for internal audit and assurance professionals, as it clarifies where ultimate responsibility lies in AI-assisted decision-making processes.

Implications for Accountability and Governance

The article emphasizes that while the functional, reasons-responsive work of judgment can be substrate-neutral and distributed across human and AI systems, the proprietary, answerable ownership of judgment is not. This has significant implications for designing governance structures and accountability frameworks in organizations leveraging AI. Internal auditors must ensure that human oversight and responsibility are clearly defined, especially in areas where AI contributes to critical judgments. The "hybrid we" of human-AI collaboration is effective for deliberation, but the final accountability must reside with a designated human or institutional principal, who designs, oversees, and occupies the normative roles that make AI outputs answerable.

Unresolved Questions and Future Considerations

The inquiry acknowledges several unresolved questions. It does not definitively conclude that future AI systems will be incapable of occupying the ownership layer, leaving this as an open empirical and conceptual question. The role of phenomenal consciousness in genuine ownership is also bracketed but not refuted. Furthermore, the article highlights that existing legal and political institutions may not be ready to authorize AI as a deputized co-author, underscoring the need for institutional redesign. Finally, the distinction between co-authorship and mere instrumentality can blur as AI becomes more deeply embedded, and the potential for distributed cognition to diffuse responsibility and obscure accountability remains a concern. These points are crucial for audit professionals to consider as they navigate the evolving landscape of AI integration.