Once in a while, a person can take an abstract concept that is seemingly too vague for formal study and offer an elegant formal definition. For the past few years, researchers have been trying to do the same for the concept of fairness in machine learning. Unfortunately, this has been trickier. Not only is the concept harder to define, but it is also impossible for a single definition to satisfy all desirable fairness metrics.
Arvind Narayanan, a computer scientist at Princeton University, has been instrumental in contextualizing different views and helping this new field establish itself. In an interview, Narayanan talks about his work on de-anonymization, the importance of statistical intuition, and the many pitfalls of AI systems.
From Quanta Magazine
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