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

2024-04-25

arXiv.org

AI Summary

Traditional accountability frameworks fail in AI system accidents due to high technical interconnectivity, ethical ambiguity, inherent uncertainty, and regulatory gaps. Method: This paper proposes Computational Reflective Equilibrium (CRE), the first formalization of the philosophical reflective equilibrium model as a computable responsibility attribution mechanism; it innovatively introduces an assertion activation control mechanism to enable sensitivity analysis and iterative refinement of responsibility distributions. Grounded in computational philosophical modeling, weighted graph-based equilibrium solving, and formal assertion logic, CRE supports continuous monitoring, reflective adjustment, and institutional optimization of responsibility allocation. Contribution/Results: Evaluated in an AI-assisted clinical diagnosis simulation, CRE demonstrates strong interpretability, ethical consistency, and dynamic adaptability—effectively reconciling technical complexity with normative accountability requirements in socio-technical AI systems.

Technology Category

• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop 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 explainability, 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.

Problem

• Establishing ethical responsibility attribution for AI incidents
• Overcoming limitations of conceptual approaches with computational framework
• Addressing accountability challenges in dynamic AI-enabled systems

Innovation

• Computational Reflective Equilibrium for responsibility attribution
• Structured analysis overcoming conceptual approach limitations
• Initial activation levels influencing equilibrium computation outcomes