Towards understanding the interactions between the trustworthiness properties of machine learning

随着机器学习模型在我们的日常生活中变得无处不在，对相关伦理问题的担忧变得越来越重要。因此，我们目睹了许多公共和私人倡议的出现，呼吁人工智能的道德发展。机器学习模型应该表现出几个值得信赖的属性，比如公平、对隐私攻击（即成员推理、属性推理、模型提取）和安全漏洞（即对抗性示例、数据中毒、海绵示例）的鲁棒性，能够解释他们的决定，或者保证被遗忘的权利。然而，这些性质相互作用的方式仍然很不明确。这对可信机器学习模型的开发构成了实质性的实际挑战。我的研究的长期愿景是创建一个框架来研究机器学习的可信度属性之间的紧张关系和收敛关系，并使用从本研究中获得的见解来开发可以同时表现出多个可信度属性的机器学习模型。更确切地说，我的研究计划将探索三种重要的背景，在这些背景中可以观察到这种紧张和融合。第一个上下文涉及事后可解释性。在这种情况下，我建议研究事后解释技术的安全和隐私漏洞，并开发可信赖的解释方法，对它们免疫。第二个上下文涉及算法的设计，以同时学习公平和隐私保护模型。在这种情况下，我提出了在数据最小化和差异隐私约束下公平决策的新训练技术。最后，第三个上下文涉及机器学习。在这种情况下，我的研究将集中在研究数据隐私和机器学习中被遗忘权之间的权衡以及健壮的数据删除算法的设计。该研究项目的成果对学术界、行业从业者和社会都很重要，因为它们将有助于人工智能系统的道德发展。它们对于值得信赖的机器学习领域以及更广泛的科学和工程领域都具有重要意义。这个研究项目的核心好处是双重的：(1)洞察机器学习的可靠性属性之间的紧张和融合；(2)开发可扩展和可信赖的机器学习模型的框架。此外，通过该研究项目培训的高素质人才将帮助加拿大公司发展值得信赖的机器学习专业知识。

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.