Home → Magazine Archive → December 2020 (Vol. 63, No. 12) → Operationalizing AI Ethics Principles → Abstract

Operationalizing AI Ethics Principles

By Cansu Canca

Communications of the ACM, Vol. 63 No. 12, Pages 18-21

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Artificial intelligence (AI) has become a part of our everyday lives from healthcare to law enforcement. AI-related ethical challenges have grown apace ranging from algorithmic bias and data privacy to transparency and accountability. As a direct reaction to these growing ethical concerns, organizations have been publishing their AI principles for ethical practice (over 100 sets and increasing). However, the multiplication of these mostly vaguely formulated principles has not proven to be helpful in guiding practice. Only by operationalizing AI principles for ethical practice can we help computer scientists, developers, and designers to spot and think through ethical issues and recognize when a complex ethical issue requires in-depth expert analysis. These operationalized AI principles for ethical practice will also help organizations confront unavoidable value trade-offs and consciously set their priorities. At the outset, it should be recognized that by their nature, AI ethics principles—as any principle-based framework—are not complete systems for ethical decision-making and not suitable for solving complex ethical problems. But once operationalized, they provide a valuable tool for detecting, conceptualizing, and devising solutions for ethical issues.

With the aim of operationalizing AI principles and guiding ethical practice, in February 2020, at the AI Ethics Lab we created the Dynamics of AI Principles,a an interactive toolbox with features to (1) sort, locate, and visualize sets of AI principles demonstrating their chronological, regional, and organizational development; (2) compare key points of different sets of principles; (3) show distribution of core principles; and (4) systematize the relation between principles.b By collecting, sorting, and comparing different sets of AI principles, we discovered a barrier for operationalization: many of the sets of AI principles mix together core and instrumental principles without regard for how they relate to each other.


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