For What It's Worth: Humans Overwrite Their Economic Self-interest to Avoid Bargaining With AI Systems

Alexander Erlei, Richeek Das, Lukas Meub, Avishek Anand, Ujwal Gadiraju

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)
21 Downloads (Pure)

Abstract

As algorithms are increasingly augmenting and substituting human decision-making, understanding how the introduction of computational agents changes the fundamentals of human behavior becomes vital. This pertains to not only users, but also those parties who face the consequences of an algorithmic decision. In a controlled experiment with 480 participants, we exploit an extended version of two-player ultimatum bargaining where responders choose to bargain with either another human, another human with an AI decision aid or an autonomous AI-system acting on behalf of a passive human proposer. Our results show strong responder preferences against the algorithm, as most responders opt for a human opponent and demand higher compensation to reach a contract with autonomous agents. To map these preferences to economic expectations, we elicit incentivized subject beliefs about their opponent's behavior. The majority of responders maximize their expected value when this is line with approaching the human proposer. In contrast, responders predicting income maximization for the autonomous AI-system overwhelmingly override economic self-interest to avoid the algorithm.

Original languageEnglish
Title of host publicationCHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages18
ISBN (Electronic)978-1-4503-9157-3
DOIs
Publication statusPublished - 2022
Event2022 CHI Conference on Human Factors in Computing Systems, CHI 2022 - Virtual, Online, United States
Duration: 30 Apr 20225 May 2022

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2022 CHI Conference on Human Factors in Computing Systems, CHI 2022
Country/TerritoryUnited States
CityVirtual, Online
Period30/04/225/05/22

Keywords

  • AI system
  • Decision Support System
  • Human-AI Interaction
  • Market Interaction
  • Online Experiment
  • Ultimatum Bargaining

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