Accountable AI with Grounded, Faithful, Consistent, Actionable Rationales: A Case Study in Clinical Trial Matching with VERDICT

The field of Artificial Intelligence, particularly with Large Language Models (LLMs), faces significant challenges in achieving accountability in decision-making processes. Traditional LLM outputs can be ungrounded, incomplete, or unfaithful to the underlying decision logic. This research proposes that accountability requires verifiable rationales, clear articulation of assumptions, policy consistency, and identification of pivotal conditions. It introduces 'self-faithfulness' as a method to test accountability, demonstrating its application in the high-stakes domain of clinical trial matching.

As AI systems become more prevalent in critical decision-making across various sectors, ensuring their accountability is paramount for trust and effective governance. This research offers a framework and a testing methodology to achieve greater transparency and justification in AI outcomes, which is crucial for managing ethical considerations and regulatory compliance.

Accountability in AI decisions is defined by the ability to examine, justify, and contest outcomes.

Forward consideration, not a verified fact.