Beyond Algorithm Aversion in Human-Machine Decision-Making

Jason W. Burton*, Mari-Klara Stein, Tina Blegind Jensen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review


A longstanding finding in the judgment and decision-making literature is that human decision performance can be improved with the help of a mechanical aid. Despite this observation and celebrated advances in computing technologies, recently presented evidence of algorithm aversion raises concerns about whether the potential of human-machine decision-making is undermined by a human tendency to discount algorithmic outputs. In this chapter, we examine the algorithm aversion phenomenon and what it means for judgment in predictive analytics. We contextualize algorithm aversion in the broader human vs. machine debate and the augmented decision-making literature before defining algorithm aversion, its implications, and its antecedents. Finally, we conclude with proposals to improve methods and metrics to help guide the development of human-machine decision-making.
Original languageEnglish
Title of host publicationJudgment in Predictive Analytics
EditorsMatthias Seifert
Number of pages24
Place of PublicationCham
Publication date2023
ISBN (Print)9783031300844
ISBN (Electronic)9783031300851
Publication statusPublished - 2023
SeriesInternational Series in Operations Research and Management Science


  • Algorithm aversion
  • Human-machine
  • Decision-making
  • Hybrid intelligence

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