AI Loyalty by Design

Author:

Aguirre Anthony1,Reiner Peter B.2,Surden Harry3,Dempsey Gaia4

Affiliation:

1. Physics, University of California, Santa Cruz

2. National Core for Neuroethics, University of British Columbia

3. Law, University of Colorado, Boulder

4. Metaculus

Abstract

Abstract Personal and professional relationships between people take a wide variety of forms, with many including both socially and legally enforced powers, responsibilities, and protections. Artificial intelligence (AI) systems are increasingly supplementing, or even replacing, people in such roles including as advisors, assistants, and (soon) doctors, lawyers, and therapists. Yet it can be quite unclear to what degree they are bound by the same sorts of responsibilities. Much has been written about fairness, accountability, and transparency in the context of AI use and trust. But largely missing from this conversation is the concept of “AI loyalty”: for whom does an AI system work? AI systems are often created by corporations or other organizations, and may be operated by an intermediary party, such as a government agency or business, but the end-users are often distinct individuals. This leads to potential conflict between the interests of the users and those of the creators or intermediaries, and, problematically, to AI systems that appear to act purely in users’ interest even when they are not. Here, we investigate the concept of “loyalty” both in human and AI systems, and advocate its central consideration in AI design. Systems for which high loyalty is appropriate should be designed, from the outset, to primarily and transparently benefit their end users, or at minimum transparently communicate unavoidable conflict-of-interest tradeoffs. This chapter discusses both market and social advantages of high-loyalty AI systems, and potential governance frameworks in which AI loyalty can be encouraged and—in appropriate contexts—required.

Publisher

Oxford University Press

Reference34 articles.

1. AI loyalty: A new paradigm for aligning stakeholder interests.;IEEE Transactions on Technology and Society,2020

2. On the Stickiness of Default Rules;33 Fla. St. U. L. Rev.,2006

3. Cavoukian, A. (2011). Privacy by design: The 7 foundational principles. Technical report. Information and Privacy Commissioner of Ontario.

4. Daw, R. (2020, October 28). Medical chatbot using OpenAI’s GPT-3 told a fake patient to kill themselves. AI News. https://artificialintelligence-news.com/2020/10/28/medical-chatbot-openai-gpt3-patient-kill-themselves/.

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