Abstract
Sensitive decisions of large-scale societal impact are increasingly being delegated to opaque software—a trend that is unlikely to slow down in the near future. The issue of fairness and bias of decision-making algorithms has thus become a multifaceted, interdisciplinary concern, attracting the attention of computer scientists, law scholars, policy makers, journalists, and many others. In this expository paper, I will outline some of the research questions we have been studying about fairness through the lens of formal methods.
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Notes
- 1.
World events at the time of writing strongly influenced my choice of example.
References
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Acknowledgements
The work described in this talk is in collaboration with a fantastic group of students and researchers: Samuel Drews, Aditya Nori, Loris D’Antoni, Justin Hsu, Calvin Smith, and David Merrell. The work described is generously supported by the National Science Foundation (NSF) grant #1704117.
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Albarghouthi, A. (2018). Fairness: A Formal-Methods Perspective. In: Podelski, A. (eds) Static Analysis. SAS 2018. Lecture Notes in Computer Science(), vol 11002. Springer, Cham. https://doi.org/10.1007/978-3-319-99725-4_1
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