Presumptuous aim attribution, conformity, and the ethics of artificial social cognition

Owen C. King

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Imagine you are casually browsing an online bookstore, looking for an interesting novel. Suppose the store predicts you will want to buy a particular novel: the one most chosen by people of your same age, gender, location, and occupational status. The store recommends the book, it appeals to you, and so you choose it. Central to this scenario is an automated prediction of what you desire. This article raises moral concerns about such predictions. More generally, this article examines the ethics of artificial social cognition—the ethical dimensions of attribution of mental states to humans by artificial systems. The focus is presumptuous aim attributions, which are defined here as aim attributions based crucially on the premise that the person in question will have aims like superficially similar people. Several everyday examples demonstrate that this sort of presumptuousness is already a familiar moral concern. The scope of this moral concern is extended by new technologies. In particular, recommender systems based on collaborative filtering are now commonly used to automatically recommend products and information to humans. Examination of these systems demonstrates that they naturally attribute aims presumptuously. This article presents two reservations about the widespread adoption of such systems. First, the severity of our antecedent moral concern about presumptuousness increases when aim attribution processes are automated and accelerated. Second, a foreseeable consequence of reliance on these systems is an unwarranted inducement of interpersonal conformity.
Original languageEnglish
Pages (from-to)25-37
Number of pages13
JournalEthics and information technology
Volume22
Issue number1
Early online date23 Sep 2019
DOIs
Publication statusPublished - 1 Mar 2020

Keywords

  • UT-Hybrid-D
  • Social cognition
  • Artificial intelligence
  • Presumptuousness
  • Recommendation
  • Recommender system
  • Collaborative filtering
  • Individuality
  • Conformity
  • Ethics
  • Aim attribution

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