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ItemDeveloping Fairness Rules for Talent Intelligence Management System( 2020-01-07)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.
ItemThe Genie in the Bottle: Different Stakeholders, Different Interpretations of Machine Learning( 2020-01-07)We explore how people developing or using a system with a machine-learning (ML) component come to understand the capabilities and challenges of ML. We draw on the social construction of technology (SCOT) tradition to frame our analysis of interviews and discussion board posts involving designers and users of a ML-supported citizen-science crowdsourcing project named Gravity Spy. We extend SCOT by anchoring our investigation in the different uses of the technology. We find that the type of understandings achieved by groups having less interaction with the technology is shaped more by outside influences and less by the specifics of the system and its role in the project. This initial understanding of how different participants understand and engage with ML points to challenges that need to be overcome to help users of a system deal with the opaque position that ML often holds in a work system.
ItemRe-thinking the Competitive Landscape of Artificial Intelligence( 2020-01-07)In recent years, Artificial Intelligence (AI) has emerged from its traditional domain of computer science research to be a management reality. This can be seen in the remarkable increase in the adoption of AI technology in organisations resulting in increased revenue, reduced costs and improved business efficiency . Despite this trend, there are still many organisations that are facing the decision whether to adopt AI. Thus, to evaluate the adoption of AI at organizational-level we draw on two-grounded theories: Technology-Organisations-Environment (TOE) framework and Diffusion of Innovation theory (DOI) to identify factors that influence the adoption of AI. Survey data collected from 208 large, medium-sized and small organisations in Australia is used to test the proposed framework. We offer a method of how examining AI over a set of organizations. Besides offering a number of important recommendations for AI adoption future directions for research in this area are also included in this paper.