Addressing jointly privacy and ethical issues in responsible machine learning

机器学习（ML）模型的成功是如此之大，以至于它们现在在我们的社会中无处不在，这引发了严重的隐私和道德问题（例如，公平性和可解释性），特别是如果它们的预测在它们可以显著影响个人的领域付诸行动。有大量的科学文献孤立地研究了ML中的隐私、公平和可解释性的概念。然而，很少有作品探索了在设计和部署ML模型时共同解决这些问题时出现的紧张关系，但也有收敛。我的研究计划的范围旨在通过调查这些不同目标的实现何时导致正和博弈以及它们相互冲突的情况来回答这个问题，并且需要设置权衡。首先，关于隐私和公平的交集，主要的挑战是设计公平的模型，同时不收集不必要的敏感属性，同时防止对训练数据或模型的隐私攻击。例如，我设想采用来自隐私的技术，如匿名化机制和数据综合方法，以整合公平性。其次，在隐私和可解释性之间的相互作用中，一个主要的紧张点是，提供解释必然会揭示更多的信息，而不是简单地输出模型的预测。在这个研究方向上，我建议在现有的解释中调查哪种类型的事后解释对隐私攻击提供了最好的鲁棒性。除了开发对策以限制此类攻击的成功之外，我还将探索哪些可解释模型（例如，线性模型，决策树或规则列表）可以提供针对推理攻击的最佳保护。第三，在公平和可解释性的交叉点上，我将致力于设计定量方法来评估后验解释背景下的公平风险。我还将详细阐述防止公平舞弊的新技术，旨在限制对手作弊的可能性。为了评估所开发方法的实际效率，只要有可能，它们将以原型的形式实现，以开源库的形式发布，并将使用机器学习和公平社区中常用的真实数据集进行实验。我的研究项目结果的社会影响可能是显著的，因为它有可能提高在高风险决策设置中部署的模型的隐私性、公平性和可解释性。此外，开发的解决方案将作为推动者，帮助加拿大公司实现在设计上保护隐私和道德的模型。最后，所进行的研究将与HQP（即博士和硕士学生）合作并为HQP的形成做出贡献。

The success of Machine Learning (ML) models is such that they are now ubiquitous in our society, which raises serious privacy and ethical issues (e.g., fairness and explainability), especially if their predictions are put into action in domains in which they can significantly affect individuals. There is a huge scientific literature that has studied in isolation, the concepts of privacy, fairness and interpretability in ML. However, very few works have explored the tensions, but also convergences, that emerge when addressing jointly these issues when designing and deploying ML models. The scope of my research program aims at answering this question by investigating when the achievement of these different objectives results in a positive sum game as well as the situations in which they conflict with each other, and a trade-off needs to be set. First, with respect to the intersection between privacy and fairness, the main challenge is to design fair models without collecting unnecessary sensitive attributes and while preventing privacy attacks on the training data or on the model. For example, I envision to adapt techniques coming from privacy such as anonymization mechanisms as well as data synthetization methods to also integrate fairness. Second, in the interactions between privacy and explainability, one of the main points of tension is that providing explanations necessarily reveal more information than simply outputting the prediction of the model. In this research direction, I propose to investigate which types of post hoc explanations among the existing ones offer the best robustness against privacy attacks. In addition of developing countermeasures to limit the success of such attacks, I will explore which families of interpretable models (e.g., linear models, decision trees or rule lists) offer the best protection against inference attacks. Third, at the crossings of fairness and explainability, I will work on the design of quantitative methods for evaluating the fairwashing risk in the context of posthoc explanations. I will also elaborate novel techniques for preventing fairwashing that aim at limiting the cheating possibilities of the adversary. To evaluate the practical efficiency of the methods developed, whenever possible they will be implemented in the form of prototypes, released in the form of an open-source library and experiments will be conducted with real datasets commonly used in the machine learning and fairness communities. The societal impact of the outcomes of my research program can be significant, as it has the potential to improve the privacy, fairness and explainability of models deployed in high-stake decision settings. In addition, the solutions developed will act as enablers by helping Canadian companies to implement models that are privacy-preserving and ethical by design. Finally, the research conducted will be done in cooperation with and contribute to the formation of HQP (i.e., PhD and master students).