Resolving Ethics Trade-offs in Implementing Responsible AI

TL;DR

This paper reviews methods for managing ethical trade-offs in AI systems, proposing a framework to identify, prioritize, and justify these trade-offs to better align AI with ethical standards and regulations.

Contribution

It introduces a comprehensive framework for managing ethical tensions in AI development, addressing the gap between ethical principles and practical implementation.

Findings

Five approaches for managing ethical trade-offs are analyzed.

The proposed framework includes identification, prioritization, and documentation of trade-offs.

Framework aims to support compliance with regulatory requirements.

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.