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To err is human: Bias salience can help overcome resistance to medical AI
16 references·0 citing·54 second hop
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References

  • 2014Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err3,203 cited
  • 2018Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management1,334 cited
  • 2013Physicians and Implicit Bias: How Doctors May Unwittingly Perpetuate Health Care Disparities1,301 cited
  • 2020Bias in data‐driven artificial intelligence systems—An introductory survey1,040 cited
  • 2020In AI we trust? Perceptions about automated decision-making by artificial intelligence1,036 cited
  • 2019Role of fairness, accountability, and transparency in algorithmic affordance643 cited
  • 2020Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook553 cited
  • 2021Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency541 cited
  • 2017Single-Paper Meta-Analysis: Benefits for Study Summary, Theory Testing, and Replicability458 cited
  • 2020Shopping intention at AI-powered automated retail stores (AIPARS)428 cited
  • 2020User Perceptions of Algorithmic Decisions in the Personalized AI System:Perceptual Evaluation of Fairness, Accountability, Transparency, and Explainability421 cited
  • 2023Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI) and higher education: A systematic review412 cited
  • 1983Bias and error in human judgment346 cited

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2021
The future of artificial intelligence at work: A review on effects of decision automation and augmentation on workers targeted by algorithms and third-party observers
336 cited
  • 2020Artificial intelligence, transparency, and public decision-making334 cited
  • 2022Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature316 cited
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