Golnoosh Farnadi

Aly Kassem

Collaborateur·rice de recherche - UWindsor

Doctorat - McGill

Co-superviseur⋅e :

Siva Reddy

Google Scholar

Saber Malekmohammadi

Collaborateur·rice de recherche - McGill

Yash Raj More

Collaborateur·rice alumni - UdeM

Selin Omur

Collaborateur·rice de recherche - McGill

Sophia Osborne

Stagiaire de recherche - McGill

Edie Pearman

Visiteur de recherche indépendant - McGill university

Nahian Rahman

Stagiaire de recherche - McGill

Soumya Sharma

Doctorat - McGill

Co-superviseur⋅e :

Adriana Romero Soriano

Site web

Google Scholar

Zhuan Shi

Postdoctorat - McGill

William St-Arnaud

Doctorat - UdeM

Co-superviseur⋅e :

Margarida Carvalho

Emma Tomiuk Tomiuk

Maîtrise recherche - McGill

Publications

Differentially Private Clustered Federated Learning

Saber Malekmohammadi

Afaf Taïk

Federated learning (FL), which is a decentralized machine learning (ML) approach, often incorporates differential privacy (DP) to provide ri… (voir plus)gorous data privacy guarantees to clients. Previous works attempted to address high structured data heterogeneity in vanilla FL settings through clustering clients (a.k.a clustered FL), but these methods remain sensitive and prone to errors, further exacerbated by the DP noise. This vulnerability makes the previous methods inappropriate for differentially private FL (DPFL) under structured data heterogeneity. To address this gap, we propose an algorithm for differentially private clustered FL, which is robust to the DP noise in the system and identifies the underlying clients’ clusters correctly. To this end, we propose to cluster clients based on both their model updates and training loss values. Furthermore, for clustering clients’ model updates at the end of the first round, our proposed approach addresses the server’s uncertainties by employing large batch sizes as well as Gaussian Mixture Models (GMM) to reduce the impact of DP and stochastic noise and avoid potential clustering errors. We provide theoretical analysis to justify our approach and evaluate it across diverse data distributions and privacy budgets. Our experimental results show the approach’s effectiveness in addressing high structured data heterogeneity in DPFL.

2025-10-07

TMLR (accepté)

Alireza Dehghanpour Farashah

Intrinsic Meets Extrinsic Fairness: Assessing the Downstream Impact of Bias Mitigation in Large Language Models

Mina Arzaghi

Florian Carichon

Large Language Models (LLMs) are increasingly deployed in sensitive domains such as finance, where intrinsic representational biases can pro… (voir plus)pagate into extrinsic harms in downstream tasks. High-stakes applications such as credit scoring are especially vulnerable, as biased model behavior can reinforce existing inequities and result in harmful disparities across demographic groups \cite{blodgett2020language}. While prior research has questioned whether intrinsic bias truly translates into extrinsic unfairness \cite{goldfarb2020intrinsic}, this connection remains poorly understood. To address this gap, we propose a four-stage evaluation framework that systematically examines the relationship between intrinsic and extrinsic fairness. In Stage 1, we establish a baseline by training models such as logistic regression, LLM embeddings, and fine-tuned classifiers without any mitigation strategy, providing reference points for fairness and accuracy. In Stage 2, we evaluate task-level mitigation through Counterfactual Data Augmentation (CDA) \cite{gallegos2024bias}, which balances gender representation by generating counterfactual training instances, allowing us to assess improvements in extrinsic fairness. In Stage 3, we adapt concept unlearning \cite{dige2024mitigating} as an intrinsic bias mitigation method, encouraging LLMs to forget socioeconomic stereotypes while preserving fluency and predictive utility, and we evaluate how this intervention impacts downstream fairness. Finally, in Stage 4, we combine CDA with unlearning to test whether dual mitigation further enhances fairness. We conduct experiments on three datasets (Adult Census Income, ACS Employment, and German Credit) using instruction-tuned LLMs (LLaMA-3.1, Phi-3, and Gemma-2) in both frozen embedding and fine-tuned classifier settings, evaluating performance with predictive accuracy and group fairness metrics, including Demographic Parity, Accuracy Parity, and Equality of Odds. Our experiments demonstrate that intrinsic bias mitigation through unlearning is highly effective; in Phi-3, for instance, it reduces gender socioeconomic stereotype gaps by 94.9\% while maintaining language fluency. In downstream tasks, unlearning consistently improves group fairness metrics while preserving predictive accuracy, whereas CDA primarily enhances demographic parity but can introduce accuracy trade-offs. For instance, on the ACS Employment dataset, unlearned Gemma-2 improved Accuracy Parity from 0.199 to 0.104 (48\% gain), and combining CDA with unlearning on Llama-3.1 reduced Demographic Parity from 0.080 to 0.014 (82\% gain). On the Adult dataset, all three models maintained accuracy above 0.82 while showing reduced fairness gaps, and on German Credit, unlearning consistently outperformed CDA by improving group fairness metrics without sacrificing predictive performance. Overall, CDA and unlearning exhibit complementary effects, with their combination yielding the strongest fairness improvements across models and datasets. This work contributes to bias mitigation and fairness in LLMs in two ways. First, we adapt concept unlearning to mitigate socioeconomic stereotyping, showing that intrinsic bias reduction improves both representational and downstream fairness. Second, we introduce a unified evaluation framework that links intrinsic and extrinsic fairness, enabling systematic comparison of mitigation strategies. The framework is flexible, applying to both fine-tuned and frozen LLMs, and offers actionable guidance for deploying fairer models in finance and other high-stakes domains.

2025-09-22

NeurIPS.cc/2025/Workshop/WiML (publié)

Neither Valid Nor Reliable? Investigating the Use of LLMs as Judges

Khaoula Chehbouni

Mohammed Haddou

Jackie Cheung

2025-09-22

NeurIPS.cc/2025/Workshop/WiML (publié)

Reasoning with Preference Constraints: A Benchmark for Language Models in Many-to-One Matching Markets

2025-09-22

NeurIPS.cc/2025/Workshop/WiML (publié)

Say It Another Way: Auditing LLMs with a User-Grounded Automated Paraphrasing Framework

Cléa Chataigner

Rebecca Ma

Prakhar Ganesh

Afaf Taïk

Elliot Creager

2025-09-22

NeurIPS.cc/2025/Workshop/WiML (publié)

Towards Democratizing LLMs: Investigating Multilingual Mixture-of-Experts Models

2025-09-22

NeurIPS.cc/2025/Workshop/WiML (publié)

Adaptation, Comparison and Practical Implementation of Fairness Schemes in Kidney Exchange Programs

William St-Arnaud

Margarida Carvalho

In Kidney Exchange Programs (KEPs), each participating patient is registered together with an incompatible donor. Donors without an incompat… (voir plus)ible patient can also register. Then, KEPs typically maximize overall patient benefit through donor exchanges. This aggregation of benefits calls into question potential individual patient disparities in terms of access to transplantation in KEPs. Considering solely this utilitarian objective may become an issue in the case where multiple exchange plans are optimal or near-optimal. In fact, current KEP policies are all-or-nothing, meaning that only one exchange plan is determined. Each patient is either selected or not as part of that unique solution. In this work, we seek instead to find a policy that contemplates the probability of patients of being in a solution. To guide the determination of our policy, we adapt popular fairness schemes to KEPs to balance the usual approach of maximizing the utilitarian objective. Different combinations of fairness and utilitarian objectives are modelled as conic programs with an exponential number of variables. We propose a column generation approach to solve them effectively in practice. Finally, we make an extensive comparison of the different schemes in terms of the balance of utility and fairness score, and validate the scalability of our methodology for benchmark instances from the literature.

2025-08-01

European Journal of Operational Research (publié)

doi.org

arxiv.org

Hallucination Detox: Sensitivity Dropout (SenD) for Large Language Model Training

Shahrad Mohammadzadeh

Juan David Guerra

Marco Bonizzato

Reihaneh Rabbany

2025-07-01

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)

doi.org

Low-Rank Adaptation Secretly Imitates Differentially Private SGD

Saber Malekmohammadi

As pre-trained language models grow in size, full fine-tuning their parameters on task adaptation data becomes increasingly impractical. To … (voir plus)address this challenge, some methods for low-rank adaptation of language models have been proposed, e.g. LoRA, which incorporates trainable low-rank decomposition matrices into only some parameters of the pre-trained model, called adapters. This approach significantly reduces the number of trainable parameters compared to fine-tuning all parameters or adapters. In this work, we look at low-rank adaptation method from the lens of data privacy. We show theoretically that the low-rank adaptation used in LoRA is equivalent to fine-tuning adapters with noisy batch gradients - just like what DPSGD algorithm does. We also quantify the variance of the injected noise as a decreasing function of adaptation rank. By establishing a Berry-Esseen type bound on the total variation distance between the injected noise distribution and a Gaussian noise distribution with the same variance, we show that the dynamics of low-rank adaptation is very close to when DPSGD is performed w.r.t the adapters. Following our theoretical findings and approved by our experimental results, we show that low-rank adaptation provides robustness to membership inference attacks w.r.t the fine-tuning data.

2025-06-10

ICML.cc/2025/Workshop/MemFM (publié)

Algorithmic Fairness Through the Lens of Metrics and Evaluation (AFME) 2024

Miriam Rateike

Awa Dieng

Jamelle Watson-Daniels

Ferdinando Fioretto

2025-04-23

Proceedings of the Algorithmic Fairness Through the Lens of Metrics and Evaluation (publié)

proceedings.mlr.press

Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in ML

Prakhar Ganeesh

Usman Gohar

Lu Cheng

With fairness concerns gaining significant attention in Machine Learning (ML), several bias mitigation techniques have been proposed, often … (voir plus)compared against each other to find the best method. These benchmarking efforts tend to use a common setup for evaluation under the assumption that providing a uniform environment ensures a fair comparison. However, bias mitigation techniques are sensitive to hyperparameter choices, random seeds, feature selection, etc., meaning that comparison on just one setting can unfairly favour certain algorithms. In this work, we show significant variance in fairness achieved by several algorithms and the influence of the learning pipeline on fairness scores. We highlight that most bias mitigation techniques can achieve comparable performance, given the freedom to perform hyperparameter optimization, suggesting that the choice of the evaluation parameters-rather than the mitigation technique itself-can sometimes create the perceived superiority of one method over another. We hope our work encourages future research on how various choices in the lifecycle of developing an algorithm impact fairness, and trends that guide the selection of appropriate algorithms.

2025-04-23

Proceedings of the Algorithmic Fairness Through the Lens of Metrics and Evaluation (publié)

doi.org

arxiv.org

Multilingual Hallucination Gaps

Cléa Chataigner

Afaf Taïk