Developing Fairness Rules for Talent Intelligence Management System

dc.contributor.author Zhang, Xi
dc.contributor.author Zhao, Yuqing
dc.contributor.author Tang, Xinlin
dc.contributor.author Zhu, Hengshu
dc.contributor.author Xiong, Hui
dc.date.accessioned 2020-01-04T08:24:14Z
dc.date.available 2020-01-04T08:24:14Z
dc.date.issued 2020-01-07
dc.description.abstract Talent management is an important business strategy, but inherently expensive due to the unique, subjective, and developing nature of each talent. Applying artificial intelligence (AI) to analyze large-scale data, talent intelligence management system (TIMS) is intended to address the talent management problems of organizations. While TIMS has greatly improved the efficiency of talent management, especially in the processes of talent selection and matching, high-potential talent discovery and talent turnover prediction, it also brings new challenges. Ethical issues, such as how to maintain fairness when designing and using TIMS, are typical examples. Through the Delphi study in a leading global AI company, this paper proposes eight fairness rules to avoid fairness risks when designing TIMS.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2020.720
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/64462
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Promises and Perils of Artificial Intelligence and Machine Learning: Disruption, Adoption, Dehumanisation, Governance, Risk and Compliance
dc.subject talent management
dc.subject artificial intelligence
dc.subject talent intelligence management system
dc.subject fairness rules
dc.title Developing Fairness Rules for Talent Intelligence Management System
dc.type Conference Paper
dc.type.dcmi Text
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