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Poster
Certification of Distributional Individual Fairness
Matthew Wicker · Vihari Piratla · Adrian Weller

Tue Dec 12 08:45 AM -- 10:45 AM (PST) @ Great Hall & Hall B1+B2 #2004
in Poster Session 1 »
Providing formal guarantees of algorithmic fairness is of paramount importance to socially responsible deployment of machine learning algorithms. In this work, we study formal guarantees, i.e., certificates, for individual fairness (IF) of neural networks. We start by introducing a novel convex approximation of IF constraints that exponentially decreases the computational cost of providing formal guarantees of local individual fairness. We highlight that prior methods are constrained by their focus on global IF certification and can therefore only scale to models with a few dozen hidden neurons, thus limiting their practical impact. We propose to certify \textit{distributional} individual fairness which ensures that for a given empirical distribution and all distributions within a $\gamma$-Wasserstein ball, the neural network has guaranteed individually fair predictions. Leveraging developments in quasi-convex optimization, we provide novel and efficient certified bounds on distributional individual fairness and show that our method allows us to certify and regularize neural networks that are several orders of magnitude larger than those considered by prior works. Moreover, we study real-world distribution shifts and find our bounds to be a scalable, practical, and sound source of IF guarantees.

Author Information

Matthew Wicker (Department of Computing, Imperial College London)
Vihari Piratla (University of Cambridge)
Adrian Weller (Cambridge, Alan Turing Institute)
Adrian Weller

Adrian Weller MBE is a Director of Research in Machine Learning at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he is Programme Director for Trust and Society. He is a Turing AI Fellow in Trustworthy Machine Learning, and heads Safe and Ethical AI at The Alan Turing Institute, the UK national institute for data science and AI. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. He serves on several boards and previously held senior roles in finance.

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