Towards understanding the interactions between the trustworthiness properties of machine learning

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As machine learning models become ubiquitous in our daily lives, concerns about associated ethical issues are becoming increasingly important. As a result, we are witnessing the emergence of many public and private initiatives, calling for an ethically aligned development of artificial intelligence. Machine learning models are expected to exhibit several trustworthiness properties such as being fair, robust against privacy attacks (i.e., membership inference, property inference, model extraction) and security vulnerabilities (i.e., adversarial examples, data poisoning, sponge examples), able to explain their decisions, or to guarantee the right to be forgotten. However, the way in which these properties interact with each other remains very poorly characterized. This represents a substantial practical challenge to the development of trustworthy machine learning models. The long-term vision of my research is to create a framework to study the tensions and convergences between the trustworthiness properties of machine learning and use the insights gained from this study to develop machine learning models that can simultaneously exhibit several trustworthiness properties. More precisely, my research program will explore three important contexts where such tensions and convergences can be observed. The first context concerns post-hoc explainability. In this setting, I propose studying the security and privacy vulnerabilities of post-hoc explanation techniques and developing trustworthy explanation methods that are immune to them. The second context concerns the design of algorithms to learn simultaneously fair and privacy-preserving models. In this setting, I propose new training techniques for fair decision-making under data minimization and differential privacy constraints. Finally, the third context concerns machine unlearning. In this setting, my research will focus on studying the trade-offs between data privacy and the right to be forgotten in machine unlearning and the design of robust data deletion algorithms. The outcomes of this research program are important for the academic community, industry practitioners, and society, as they will contribute to an ethically aligned development of artificial intelligence systems. They are significant for the trustworthy machine learning field and, more generally, for science and engineering. The core benefits of this research program are twofold: (1) insights into the tensions and convergences between trustworthiness properties of machine learning and (2) a framework to develop scalable and trustworthy machine learning models. Furthermore, the highly qualified personnel trained through this research program will help Canadian companies develop expertise in trustworthy machine learning.