Interview featured in the NY Times:
“Q: Some people have argued that algorithms eliminate discrimination because they make decisions based on data, free of human bias. Others say algorithms reflect and perpetuate human biases. What do you think?
A: Algorithms do not automatically eliminate bias. Suppose a university, with admission and rejection records dating back for decades and faced with growing numbers of applicants, decides to use a machine learning algorithm that, using the historical records, identifies candidates who are more likely to be admitted. Historical biases in the training data will be learned by the algorithm, and past discrimination will lead to future discrimination.
(…) Q: You have studied both privacy and algorithm design, and co-wrote a paper, “Fairness Through Awareness,” that came to some surprising conclusions about discriminatory algorithms and people’s privacy. Could you summarize those?
A: “Fairness Through Awareness” makes the observation that sometimes, in order to be fair, it is important to make use of sensitive information while carrying out the classification task. This may be a little counterintuitive: The instinct might be to hide information that could be the basis of discrimination.
Q: What’s an example?
A: Suppose we have a minority group in which bright students are steered toward studying math, and suppose that in the majority group bright students are steered instead toward finance. An easy way to find good students is to look for students studying finance, and if the minority is small, this simple classification scheme could find most of the bright students.
But not only is it unfair to the bright students in the minority group, it is also low utility. Now, for the purposes of finding bright students, cultural awareness tells us that “minority+math” is similar to “majority+finance.” A classification algorithm that has this sort of cultural awareness is both more fair and more useful.
Fairness means that similar people are treated similarly. A true understanding of who should be considered similar for a particular classification task requires knowledge of sensitive attributes, and removing those attributes from consideration can introduce unfairness and harm utility.
(…) Q: Another recent example of the problem came from Carnegie Mellon University, where researchers found that Google’s advertising system showed an ad for a career coaching service for “$200k+” executive jobs to men much more often than to women. What did that study tell us about these issues?
A: The paper is very thought-provoking. The examples described in the paper raise questions about how things are done in practice. I am currently collaborating with the authors and others to consider the differing legal implications of several ways in which an advertising system could give rise to these behaviors.
(…)
Q: Should computer science education include lessons on how to be aware of these issues and the various approaches to addressing them?
A: Absolutely!”