Recommended Reads

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.