FRICE: Enhancing AI Trust through a Robust Accountability System

Abstract

In the paper presented, a mathematical framework has been formulated on the basis of the FRICE Model to allow the evaluation of accountability in AI systems across five dimensions of assessment-Fairness, Robustness, Impact, Compliance and Explanatory Effectiveness-becoming necessary currently. The framework proposed here focuses on algorithmic assessment methods to ensure transparency, reliability and ethical governance in AI-based decision-making processes. Accountability score computation in the FRICE model employs a structured mathematical method, such as fairness-by Statistical Parity Difference (SPD) and Equal Opportunity Difference (EOD)-robustness by adversarial accuracy tests-impact by positive and negative outcome assessments-compliance and explanatory effectiveness. These features embed the principles of ethics into the system by ensuring compliance with legal stipulations and clarity in decision explanations. This framework takes weighted aggregation techniques incorporating parameters into account and translates them into a holistic accountability score, reflecting the performance of the system as a whole. Each parameter is presented together with its detailed formulas to ensure reproducibility and adaptation to various AI applications. Trade-offs will equally hinge on how ethical considerations are deeply integrated into the designs with fairness, inclusivity, and transparency to mitigate biases and ensure equitable results.