TURNING OFF YOUR BETTER JUDGEMENT-CONFORMITY TO ALGORITHMIC RECOMMENDATIONS

Yotam Liel; Lior Zalmanson

doi:10.5465/AMPROC.2023.277bp

TURNING OFF YOUR BETTER JUDGEMENT-CONFORMITY TO ALGORITHMIC RECOMMENDATIONS

Yotam Liel, Lior Zalmanson

Tel Aviv University

Research output: Contribution to journal › Conference article › peer-review

Abstract

This research investigates the extent to which humans rely on algorithmic advice, even when it contradicts their better judgment. Drawing from classic research on social conformity, it examines workers performing simple and objective tasks' adherence to clearly erroneous algorithmic recommendations. Results from three studies (n = 1,085) show that substantial percentages of workers follow erroneous algorithmic recommendations (8.8%-26.5% of tasks). Furthermore, workers are more likely to conform to algorithmic recommendations than to identical recommendations generated by humans. The study also finds that conformity decreases when workers perceive the real-life impact of their decisions as high.

Original language	English
Journal	Academy of Management Annual Meeting Proceedings
Volume	2023
Issue number	1
DOIs	https://doi.org/10.5465/AMPROC.2023.277bp
State	Published - 2023
Event	83rd Annual Meeting of the Academy of Management, AOM 2023 - Boston, United States Duration: 4 Aug 2023 → 8 Aug 2023

All Science Journal Classification (ASJC) codes

Management Information Systems
Management of Technology and Innovation
Industrial relations

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title = "TURNING OFF YOUR BETTER JUDGEMENT-CONFORMITY TO ALGORITHMIC RECOMMENDATIONS",

abstract = "This research investigates the extent to which humans rely on algorithmic advice, even when it contradicts their better judgment. Drawing from classic research on social conformity, it examines workers performing simple and objective tasks' adherence to clearly erroneous algorithmic recommendations. Results from three studies (n = 1,085) show that substantial percentages of workers follow erroneous algorithmic recommendations (8.8%-26.5% of tasks). Furthermore, workers are more likely to conform to algorithmic recommendations than to identical recommendations generated by humans. The study also finds that conformity decreases when workers perceive the real-life impact of their decisions as high.",

author = "Yotam Liel and Lior Zalmanson",

note = "Publisher Copyright: {\textcopyright} 2023, Academy of Management. All rights reserved.; 83rd Annual Meeting of the Academy of Management, AOM 2023 ; Conference date: 04-08-2023 Through 08-08-2023",

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AB - This research investigates the extent to which humans rely on algorithmic advice, even when it contradicts their better judgment. Drawing from classic research on social conformity, it examines workers performing simple and objective tasks' adherence to clearly erroneous algorithmic recommendations. Results from three studies (n = 1,085) show that substantial percentages of workers follow erroneous algorithmic recommendations (8.8%-26.5% of tasks). Furthermore, workers are more likely to conform to algorithmic recommendations than to identical recommendations generated by humans. The study also finds that conformity decreases when workers perceive the real-life impact of their decisions as high.

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M3 - مقالة من مؤنمر

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VL - 2023

JO - Academy of Management Annual Meeting Proceedings

JF - Academy of Management Annual Meeting Proceedings

IS - 1

T2 - 83rd Annual Meeting of the Academy of Management, AOM 2023

Y2 - 4 August 2023 through 8 August 2023

ER -

TURNING OFF YOUR BETTER JUDGEMENT-CONFORMITY TO ALGORITHMIC RECOMMENDATIONS

Abstract

All Science Journal Classification (ASJC) codes

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