What Does Building a Fair AI Really Entail?
This article analyzes AI fairness as both essential in itself and as a way to solve the issue of trust in AI systems. The author advocates for an interdisciplinary approach, with computer science and the social sciences working together. Three recommendations are outlined: (1) train managers to act as “devil’s advocates” by evaluating algorithmic decision-making using common sense and intuitive notions of what is right and wrong; (2) require leaders to articulate their companies’ values and moral norms to help inform compromises between utility and human values in AI deployment; (3) hold data scientists and organizational leaders responsible for collaborating to evaluate the fairness of AI models both against technical definitions and broader company values.
David De Cremer, Harvard Business Review
September 3, 2020