Beyond Algorithm Aversion in Human-Machine Decision-Making

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

*Corresponding author af dette arbejde

Publikation: Bidrag til bog/antologi/rapportBidrag til bog/antologiForskningpeer review

Abstract

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.
OriginalsprogEngelsk
TitelJudgment in Predictive Analytics
RedaktørerMatthias Seifert
Antal sider24
UdgivelsesstedCham
ForlagSpringer
Publikationsdato2023
Sider3–26
Kapitel1
ISBN (Trykt)9783031300844
ISBN (Elektronisk)9783031300851
DOI
StatusUdgivet - 2023
NavnInternational Series in Operations Research and Management Science
Vol/bind343
ISSN0884-8289

Emneord

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

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