Addressing algorithm bias in AI-Driven customer management

Shahriar Akter (University of Wollongong, Australia)

Katina Michael (Arizona State University, USA)

Abstract:

Research on AI has gained momentum in recent years. Many scholars and practitioners increasingly highlight the dark sides of AI, particularly related to algorithm bias. This presentation will highlight situations in which AI-enabled analytics systems make biased decisions against customers based on gender, race, religion, age, nationality or socioeconomic status. Based on ongoing research, the speakers will also discuss two approaches (i.e., a priori and post-hoc) to overcome such biases in AI-driven customer management.

Time: 9pm on 16th June Sydney, Australia time zone

Venue: Swansea University

Shahriar Akter’s Bio: Dr Shahriar Akter is an Associate Professor of Marketing Analytics & Innovation at the School of Business at UOW. He was awarded his PhD from the UNSW Business School Australia, with a doctoral fellowship in research methods from the University of Oxford. He has published in leading business journals (40+ A or A ranked articles in the ABDC list) with 90+ publications. He is one of the top 2% business analytics researchers across the world, according to the 2020 Stanford University Ranking. His current h-Index is 28 with 6000+ citations in the last 5 years. He is a Visiting Professor of the Toulouse Business School (France), the University of Michigan and Shanghai Jiao Tong University Joint Institute (China) and Karlstad Business School (Sweden).

Katina Michael’s Bio: Katina Michael is a professor at Arizona State University, holding a joint appointment in the School for the Future of Innovation in Society and School of Computing, Informatics and Decisions Systems Engineering. She is also the director of the Society Policy Engineering Collective (SPEC) and the Founding Editor-in-Chief of the IEEE Transactions on Technology and Society. Katina is a senior member of the IEEE and a Public Interest Technology advocate who studies the social implications of technology.