In this interview, Professor Kalinda Ukanwa illuminates ways to screen for bias in consumer-driven algorithms, the importance of trust between business entities and consumers, and why it pays to correct biased algorithms, rather than leave them unchecked. Drawing an analogy to the automobile's evolution, she highlights the need for safety measures in AI development, showing that while biased algorithms might be profitable in the short term, they are detrimental in the long term, affecting consumer trust and company demand. She advocates for third-party oversight to align consumer and firm perceptions of fairness, referencing a case study on the Apple credit card controversy to illustrate how unfounded, biased perceptions can arise. She calls for interdisciplinary research to explore AI's broader implications and urged business leaders to thoughtfully manage AI's potential and risks, ensuring responsible integration of this new transformative technology.
Professor Ukanwa of the University of Southern California has studied algorithms and bias for over a decade in the field of marketing. She joined the Inequality in the Digital Age conference organized by the Race, Gender & Equity initiative in March this year.
