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  • 3 - What Can Agents Reasonably Endorse?

  • from Part II - Respecting Persons, What We Owe Them
  • Alan Rubel, University of Wisconsin, Madison, Clinton Castro, Florida International University, Adam Pham, California Institute of Technology
  • Book:
    Algorithms and Autonomy
    Published online:
    17 May 2021
    Print publication:
    20 May 2021, pp 45-69
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  • Summary

    Chapter 3 takes the conception of autonomy outlined in chapter 2 and explains how it grounds moral evaluation of algorithmic systems. It begins by offering a view of what it takes to respect autonomy and to respect persons in virtue of their autonomy, drawing on a number of different normative moral theories. The argument starts with a description of a K-12 teacher evaluation program from Washington, DC. It then considers several puzzles about the case. Next, the chapter provides an account of respecting autonomy and what that means for individuals’ moral claims. It explains how that account can help us understand the DC case, and we will offer a general account of the moral requirements of algorithmic systems. Specifically, we offer the Reasonable Endorsement Test, according to which an action is morally permissible only if it would be allowed by principles that each person subject to it could reasonably endorse. The chapter applies that test to the Loomis, Houston Schools, and Wagner cases. Finally, the chapter explains why the book does not focus directly on “fairness.”