Portrait of Maryam Babaei

Maryam Babaei

maryam.babaei@mila.quebec
PhD - École de technologie suprérieure
Supervisor
Ulrich Aivodji
Research Topics
Trustworthy Machine Learning
GitHub

Publications

Quantifying the Privacy of Counterfactuals by Leveraging Membership Inference Attacks Against Synthetic Data
Maryam Babaei
Yingke Wang
Hadrien Lautraite
Héber H. Arcolezi
Ulrich Aivodji
Sébastien Gambs
Counterfactuals are typically used in high-stakes decision areas to explain a machine learning model by showing how changes to the user prof… (see more)iles result in the desired outcome. However, explaining the model's decisions through counterfactuals can also be exploited by an adversary to conduct privacy attacks against the model or its training data. Drawing on the analogy that counterfactuals provide realistic substitutes for real training data, similar to synthetic data, we demonstrate in this paper how it is possible to successfully perform privacy attacks on counterfactuals by drawing on the attacks developed against synthetic data. More precisely, we investigate the effectiveness of the membership inference attacks designed for synthetic data on various types of counterfactuals. Additionally, while existing membership inference attacks against counterfactuals usually require to be able to query the model, we show how it is possible to perform successful membership inference attacks using only a set of counterfactuals, with no access to the model from which they are generated. Our results demonstrate that model developers should be more cautious when releasing counterfactuals to various users, as it can lead to a privacy breach.
2026-06-24
ACM Conference on Fairness, Accountability, and Transparency (published)
doi.org
arxiv.org
Survey on <scp>AI</scp> Ethics: A Socio‐Technical Perspective
Dave Mbiazi
Meghana Bhange
Maryam Babaei
Ivaxi Sheth
Patrik Joslin Kenfack
Samira Ebrahimi Kahou
Abstract The past decade has observed a significant advancement in AI, with deep learning‐based models being deployed in diverse scenarios… (see more), including safety‐critical applications. As these AI systems become deeply embedded in our societal infrastructure, the repercussions of their decisions and actions have significant consequences, making the ethical implications of AI deployment highly relevant and essential. The ethical concerns associated with AI are multifaceted, including challenging issues of fairness, privacy and data protection, responsibility and accountability, safety and robustness, transparency and explainability, and environmental impact. These principles together form the foundations of ethical AI considerations that concern every stakeholder in the AI system lifecycle. In light of the present ethical and future x‐risk concerns, governments have shown increasing interest in establishing guidelines for the ethical deployment of AI. This work unifies the current and future ethical concerns of deploying AI into society. While we acknowledge and appreciate the technical surveys for each of the ethical principles concerned, in this paper, we aim to provide a comprehensive overview that not only addresses each principle from a technical point of view but also discusses them from a social perspective.
2025-11-01
Computational Intelligence (published)
doi.org
arxiv.org