Explainable Recommendations and Calibrated Trust: Two Systematic User Errors

Mohammad Naiseh; Deniz Cemiloglu; Dena Al Thani; Nan Jiang; Raian Ali

doi:10.1109/MC.2021.3076131

Explainable Recommendations and Calibrated Trust: Two Systematic User Errors

Mohammad Naiseh
, Deniz Cemiloglu
, Dena Al Thani
, Nan Jiang
, Raian Ali

CSE Information & Computing Technology

Research output: Contribution to specialist publication › Article

22 Citations (Scopus)

Abstract

The increased adoption of collaborative human-artificial intelligence decision-making tools triggered a need to explain recommendations for safe and effective collaboration. We explore how users interact with explanations and why trust-calibration errors occur, taking clinical decision-support systems as a case study.

Original language	English
Pages	28-37
Number of pages	10
Volume	54
No.	10
Specialist publication	Computer
DOIs	https://doi.org/10.1109/MC.2021.3076131
Publication status	Published - Oct 2021

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10.1109/MC.2021.3076131

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title = "Explainable Recommendations and Calibrated Trust: Two Systematic User Errors",

abstract = "The increased adoption of collaborative human-artificial intelligence decision-making tools triggered a need to explain recommendations for safe and effective collaboration. We explore how users interact with explanations and why trust-calibration errors occur, taking clinical decision-support systems as a case study.",

author = "Mohammad Naiseh and Deniz Cemiloglu and \{Al Thani\}, Dena and Nan Jiang and Raian Ali",

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year = "2021",

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doi = "10.1109/MC.2021.3076131",

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AU - Naiseh, Mohammad

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AU - Jiang, Nan

AU - Ali, Raian

PY - 2021/10

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N2 - The increased adoption of collaborative human-artificial intelligence decision-making tools triggered a need to explain recommendations for safe and effective collaboration. We explore how users interact with explanations and why trust-calibration errors occur, taking clinical decision-support systems as a case study.

AB - The increased adoption of collaborative human-artificial intelligence decision-making tools triggered a need to explain recommendations for safe and effective collaboration. We explore how users interact with explanations and why trust-calibration errors occur, taking clinical decision-support systems as a case study.

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DO - 10.1109/MC.2021.3076131

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JO - Computer

JF - Computer

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Explainable Recommendations and Calibrated Trust: Two Systematic User Errors

Abstract

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