research-article

Designing Fair AI in Human Resource Management: Understanding Tensions Surrounding Algorithmic Evaluation and Envisioning Stakeholder-Centered Solutions

Authors:

Kartik Hosanagar,

Joonhwan LeeAuthors Info & Claims

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

Article No.: 51, Pages 1 - 22

https://doi.org/10.1145/3491102.3517672

Published: 28 April 2022 Publication History

Abstract

Enterprises have recently adopted AI to human resource management (HRM) to evaluate employees’ work performance evaluation. However, in such an HRM context where multiple stakeholders are complexly intertwined with different incentives, it is problematic to design AI reflecting one stakeholder group's needs (e.g., enterprises, HR managers). Our research aims to investigate what tensions surrounding AI in HRM exist among stakeholders and explore design solutions to balance the tensions. By conducting stakeholder-centered participatory workshops with diverse stakeholders (including employees, employers/HR teams, and AI/business experts), we identified five major tensions: 1) divergent perspectives on fairness, 2) the accuracy of AI, 3) the transparency of the algorithm and its decision process, 4) the interpretability of algorithmic decisions, and 5) the trade-off between productivity and inhumanity. We present stakeholder-centered design ideas for solutions to mitigate these tensions and further discuss how to promote harmony among various stakeholders at the workplace.

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Matthew UKazaure JNdukwu CEbong GNwanakwaugwu ANwamouh U(2024)Artificial Intelligence Educational Pedagogy DevelopmentEducational Perspectives on Digital Technologies in Modeling and Management10.4018/979-8-3693-2314-4.ch003(65-93)Online publication date: 7-Jan-2024
https://doi.org/10.4018/979-8-3693-2314-4.ch003
Nguyen TAlam SHu CAlbiston CSalehi N(2024)Definitions of Fairness Differ Across Socioeconomic Groups & Shape Perceptions of Algorithmic DecisionsProceedings of the ACM on Human-Computer Interaction10.1145/36870588:CSCW2(1-31)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687058
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cover image ACM Conferences

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

April 2022

10459 pages

ISBN:9781450391573

DOI:10.1145/3491102

Editors:
Simone Barbosa
PUC-Rio, Brazil
,
Cliff Lampe
University of Michigan, USA
,
Caroline Appert
Université Paris-Saclay, France
,
David A. Shamma
Toyota Research Institute, USA
,
Steven Drucker
Microsoft Research, USA
,
Julie Williamson
University of Glasgow, UK
,
Koji Yatani
University of Tokyo, Japan

Copyright © 2022 ACM.

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Published: 28 April 2022

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the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea

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CHI '22

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SIGCHI

CHI '22: CHI Conference on Human Factors in Computing Systems

April 29 - May 5, 2022

LA, New Orleans, USA

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Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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CHI '25

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sigchi

CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

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Cited By

Saxena RSrivastava VBharti DSingh RKumar A(2025)Artificial intelligence for renewable energy strategies and techniquesComputer Vision and Machine Intelligence for Renewable Energy Systems10.1016/B978-0-443-28947-7.00002-1(17-39)Online publication date: 2025
https://doi.org/10.1016/B978-0-443-28947-7.00002-1
Matthew UKazaure JNdukwu CEbong GNwanakwaugwu ANwamouh U(2024)Artificial Intelligence Educational Pedagogy DevelopmentEducational Perspectives on Digital Technologies in Modeling and Management10.4018/979-8-3693-2314-4.ch003(65-93)Online publication date: 7-Jan-2024
https://doi.org/10.4018/979-8-3693-2314-4.ch003
Nguyen TAlam SHu CAlbiston CSalehi N(2024)Definitions of Fairness Differ Across Socioeconomic Groups & Shape Perceptions of Algorithmic DecisionsProceedings of the ACM on Human-Computer Interaction10.1145/36870588:CSCW2(1-31)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687058
Valentine MPratt AHinds RBernstein M(2024)The Algorithm and the Org Chart: How Algorithms Can Conflict with Organizational StructuresProceedings of the ACM on Human-Computer Interaction10.1145/36869038:CSCW2(1-31)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3686903
Das Swain VSaha K(2024)Teacher, Trainer, Counsel, Spy: How Generative AI can Bridge or Widen the Gaps in Worker-Centric Digital Phenotyping of WellbeingProceedings of the 3rd Annual Meeting of the Symposium on Human-Computer Interaction for Work10.1145/3663384.3663401(1-13)Online publication date: 25-Jun-2024
https://doi.org/10.1145/3663384.3663401
Vincenzi BStumpf STaylor ANakao Y(2024)Lay User Involvement in Developing Human-centric Responsible AI Systems: When and How?ACM Journal on Responsible Computing10.1145/36525921:2(1-25)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3652592
Lee CLee MMutlu B(2024)The AI-DEC: A Card-based Design Method for User-centered AI ExplanationsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661576(1010-1028)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661576
Deck LSchoeffer JDe-Arteaga MKühl N(2024)A Critical Survey on Fairness Benefits of Explainable AIProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658990(1579-1595)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658990
Kwon SYoo DKang Y(2024)Spiritual AI: Exploring the Possibilities of a Human-AI Interaction Beyond Productive GoalsExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650743(1-8)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3650743
Park H(2024)Re-examining User Burden in Human-AI Interaction: Focusing on a Domain-Specific ApproachExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3638186(1-4)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3638186
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