skip to main content
10.1145/3287560.3287591acmconferencesArticle/Chapter ViewAbstractPublication PagesfacctConference Proceedingsconference-collections
research-article

Robot Eyes Wide Shut: Understanding Dishonest Anthropomorphism

Authors Info & Claims
Published:29 January 2019Publication History

ABSTRACT

The goal of this paper is to advance design, policy, and ethics scholarship on how engineers and regulators can protect consumers from deceptive robots and artificial intelligences that exhibit the problem of dishonest anthropomorphism. The analysis expands upon ideas surrounding the principle of honest anthropomorphism originally formulated by Margot Kaminsky, Mathew Ruben, William D. Smart, and Cindy M. Grimm in their groundbreaking Maryland Law Review article, "Averting Robot Eyes." Applying boundary management theory and philosophical insights into prediction and perception, we create a new taxonomy that identifies fundamental types of dishonest anthropomorphism and pinpoints harms that they can cause. To demonstrate how the taxonomy can be applied as well as clarify the scope of the problems that it can cover, we critically consider a representative series of ethical issues, proposals, and questions concerning whether the principle of honest anthropomorphism has been violated.

Index Terms

  1. Robot Eyes Wide Shut: Understanding Dishonest Anthropomorphism

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      FAT* '19: Proceedings of the Conference on Fairness, Accountability, and Transparency
      January 2019
      388 pages
      ISBN:9781450361255
      DOI:10.1145/3287560

      Copyright © 2019 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 29 January 2019

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

      Upcoming Conference

      FAccT '24

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader