The Ethics of AI Value Chains

TL;DR

This paper introduces AI value chains as an integrative framework for understanding and addressing ethical issues in AI systems across various contexts, emphasizing the importance of actor context, resource types, and ethical scope.

Contribution

It develops a conceptual framework of AI value chains, distinguishes it from supply chains, and provides an integrative review of ethical concerns with future directions for ethical AI practices.

Findings

AI value chains offer a comprehensive view of ethical issues in AI.

A review of 67 sources highlights key ethical concerns across AI value chains.

Recommendations for future research and practice to enhance ethical AI development.

Abstract

Researchers, practitioners, and policymakers with an interest in AI ethics need more integrative approaches for studying and intervening in AI systems across many contexts and scales of activity. This paper presents AI value chains as an integrative concept that satisfies that need. To more clearly theorize AI value chains and conceptually distinguish them from supply chains, we review theories of value chains and AI value chains from the strategic management, service science, economic geography, industry, government, and applied research literature. We then conduct an integrative review of a sample of 67 sources that cover the ethical concerns implicated in AI value chains. Building upon the findings of our integrative review, we recommend three future directions that researchers, practitioners, and policymakers can take to advance more ethical practices across AI value chains. We urge AI ethics researchers and practitioners to move toward value chain perspectives that situate actors in context, account for the many types of resources involved in co-creating AI systems, and integrate a wider range of ethical concerns across contexts and scales.