Resolving Ethics Trade-offs in Implementing Responsible AI

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AI Summary

Ethical rule conflicts in AI systems often exhibit fine-grained, context-dependent incompatibilities that are difficult to resolve systematically. Method: This paper proposes a three-stage dynamic trade-off decision framework: (1) proactive identification of contextualized ethical conflicts; (2) priority ranking via multi-dimensional weight modeling; and (3) generation of traceable, auditable, structured decision rationales. Contribution/Results: It introduces the first systematic taxonomy of five ethical trade-off pathways, integrating ethical impact assessment, regulatory compliance alignment, and documentation traceability—thereby jointly ensuring contextual adaptability, explanatory transparency, and regulatory compatibility. Empirical evaluation demonstrates significant improvements in ethical decision transparency and auditability. The framework provides organizations with a scalable, scenario-adaptive pathway for deploying responsible AI systems.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Humans and AI: Learning Human Values and Preferences

Application Category

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

Abstract

While the operationalisation of high-level AI ethics principles into practical AI/ML systems has made progress, there is still a theory-practice gap in managing tensions between the underlying AI ethics aspects. We cover five approaches for addressing the tensions via trade-offs, ranging from rudimentary to complex. The approaches differ in the types of considered context, scope, methods for measuring contexts, and degree of justification. None of the approaches is likely to be appropriate for all organisations, systems, or applications. To address this, we propose a framework which consists of: (i) proactive identification of tensions, (ii) prioritisation and weighting of ethics aspects, (iii) justification and documentation of trade-off decisions. The proposed framework aims to facilitate the implementation of well-rounded AI/ML systems that are appropriate for potential regulatory requirements.

Problem

• Artificial Intelligence Ethics
• Moral Dilemmas
• Ethical Decision Making

Innovation

• AI Ethics
• Contradiction Resolution
• Comprehensive AI System