When Emotion AI Meets Strategic Users

76 Pages Posted: 22 Sep 2022 Last revised: 3 Apr 2024

See all articles by Yifan Yu

Yifan Yu

The University of Texas at Austin; Amazon

Wendao Xue

University of Texas at Austin - Department of Information, Risk and Operations Management; University of Washington - Department of Economics

Lin Jia

School of Management and Economics, Beijing Institute of Technology

Yong Tan

University of Washington - Michael G. Foster School of Business

Date Written: September 13, 2022

Abstract

When organizations adopt artificial intelligence (AI) to recognize individuals' negative emotions and accordingly allocate limited resources, strategic users are incentivized to game the system by misrepresenting their emotions. The value of AI in automating such emotion-driven allocation may be undermined by gaming behavior, algorithmic noise in emotion detection, and the spillover effect of negative emotions. We develop a game-theoretical model to understand emotion AI adoption, particularly in customer care, and analyze the design of the associated allocation policies. We find that adopting emotion AI is valuable if the spillover effect of negative emotions is negligible compared to resource misallocation loss, regardless of algorithmic noise and gaming behavior. We also quantify the welfare impacts of emotion AI on the users, organization, and society. Notably, a stronger AI is not always socially desirable and regulation on emotion-driven allocation is needed. Finally, we characterize conditions under which leveraging the AI system is preferred to hiring human employees in emotion-driven allocation. We also explore the alternative application of using emotion AI to monitor strategic employees and compare it with hiring a human manager for monitoring. Intriguingly, algorithmic noise may increase the profit of AI monitoring. Our work provides implications for designing, adopting, and regulating emotion AI.

Keywords: Emotion AI, strategic users, signaling game, muddled information

JEL Classification: D82, M15, M21

Suggested Citation

Yu, Yifan and Xue, Wendao and Jia, Lin and Tan, Yong, When Emotion AI Meets Strategic Users (September 13, 2022). Available at SSRN: https://ssrn.com/abstract=4218083 or http://dx.doi.org/10.2139/ssrn.4218083

Yifan Yu (Contact Author)

The University of Texas at Austin ( email )

2317 Speedway
Austin, TX Texas 78712
United States

Amazon ( email )

Wendao Xue

University of Texas at Austin - Department of Information, Risk and Operations Management ( email )

CBA 5.202
Austin, TX 78712
United States

University of Washington - Department of Economics ( email )

Box 353330
Seattle, WA 98195-3330
United States

Lin Jia

School of Management and Economics, Beijing Institute of Technology

5 South Zhongguancun Street
Beijing, Beijing 100081
China

Yong Tan

University of Washington - Michael G. Foster School of Business ( email )

Box 353226
Seattle, WA 98195-3226
United States

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