Attributing Responsibility in AI-Induced Incidents: A Computational Reflective Equilibrium Framework for Accountability

TL;DR

This paper introduces a Computational Reflective Equilibrium framework to improve responsibility attribution in AI incidents, addressing ethical, technical, and regulatory challenges with a structured, adaptable approach demonstrated through a medical AI case study.

Contribution

It proposes a novel CRE-based method for responsibility attribution in AI incidents, overcoming limitations of traditional approaches with a focus on coherence, traceability, and adaptability.

Findings

The framework effectively models responsibility distribution in AI incidents.

Different initializations lead to varied responsibility attributions.

The approach enhances accountability analysis in complex AI systems.

Abstract

The pervasive integration of Artificial Intelligence (AI) has introduced complex challenges in the responsibility and accountability in the event of incidents involving AI-enabled systems. The interconnectivity of these systems, ethical concerns of AI-induced incidents, coupled with uncertainties in AI technology and the absence of corresponding regulations, have made traditional responsibility attribution challenging. To this end, this work proposes a Computational Reflective Equilibrium (CRE) approach to establish a coherent and ethically acceptable responsibility attribution framework for all stakeholders. The computational approach provides a structured analysis that overcomes the limitations of conceptual approaches in dealing with dynamic and multifaceted scenarios, showcasing the framework's traceability, coherence, and adaptivity properties in the responsibility attribution process. We examine the pivotal role of the initial activation level associated with claims in equilibrium computation. Using an AI-assisted medical decision-support system as a case study, we illustrate how different initializations lead to diverse responsibility distributions. The framework offers valuable insights into accountability in AI-induced incidents, facilitating the development of a sustainable and resilient system through continuous monitoring, revision, and reflection.