Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/64462

Developing Fairness Rules for Talent Intelligence Management System

File Size Format  
0581.pdf 593.38 kB Adobe PDF View/Open

Item Summary

Title:Developing Fairness Rules for Talent Intelligence Management System
Authors:Zhang, Xi
Zhao, Yuqing
Tang, Xinlin
Zhu, Hengshu
Xiong, Hui
Keywords:Promises and Perils of Artificial Intelligence and Machine Learning: Disruption, Adoption, Dehumanisation, Governance, Risk and Compliance
talent management
artificial intelligence
talent intelligence management system
fairness rules
Date Issued:07 Jan 2020
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/64462
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.720
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Promises and Perils of Artificial Intelligence and Machine Learning: Disruption, Adoption, Dehumanisation, Governance, Risk and Compliance


Please email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons