Portrait de Afaf Taïk

Afaf Taïk

Membre académique associé
Professeur adjoint, Université de Sherbrooke, Département de génie électrique et informatique
Sujets de recherche
Apprentissage fédéré
Équité
Éthique de l'IA
IA responsable
Vie privée
Google Scholar

Étudiants actuels

Noor Hammodi
Collaborateur·rice de recherche - UQAM

Publications

Fairness in Federated Learning: Fairness for Whom?
Afaf Taïk
Khaoula Chehbouni
Golnoosh Farnadi
Fairness in federated learning has emerged as a rapidly growing area of research, with numerous works proposing formal definitions and algor… (voir plus)ithmic interventions. Yet, despite this technical progress, fairness in FL is often defined and evaluated in ways that abstract away from the sociotechnical contexts in which these systems are deployed. In this paper, we argue that existing approaches tend to optimize narrow system level metrics, such as performance parity or contribution-based rewards, while overlooking how harms arise throughout the FL lifecycle and how they impact diverse stakeholders. We support this claim through a critical analysis of the literature, based on a systematic annotation of papers for their fairness definitions, design decisions, evaluation practices, and motivating use cases. Our analysis reveals five recurring pitfalls: 1) fairness framed solely through the lens of server client architecture, 2) a mismatch between simulations and motivating use-cases and contexts, 3) definitions that conflate protecting the system with protecting its users, 4) interventions that target isolated stages of the lifecycle while neglecting upstream and downstream effects, 5) and a lack of multi-stakeholder alignment where multiple fairness definitions can be relevant at once. Building on these insights, we propose a harm centered framework that links fairness definitions to concrete risks and stakeholder vulnerabilities. We conclude with recommendations for more holistic, context-aware, and accountable fairness research in FL.
2025-10-14
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (publié)
doi.org
arxiv.org
Mitigating Disparate Impact of Differential Privacy in Federated Learning through Robust Clustering
Saber Malekmohammadi
Afaf Taïk
Golnoosh Farnadi
Federated Learning (FL) is a decentralized machine learning (ML) approach that keeps data localized and often incorporates Differential Priv… (voir plus)acy (DP) to enhance privacy guarantees. Similar to previous work on DP in ML, we observed that differentially private federated learning (DPFL) introduces performance disparities, particularly affecting minority groups. Recent work has attempted to address performance fairness in vanilla FL through clustering, but this method remains sensitive and prone to errors, which are further exacerbated by the DP noise in DPFL. To fill this gap, in this paper, we propose a novel clustered DPFL algorithm designed to effectively identify clients' clusters in highly heterogeneous settings while maintaining high accuracy with DP guarantees. To this end, we propose to cluster clients based on both their model updates and training loss values. Our proposed approach also addresses the server's uncertainties in clustering clients' model updates by employing larger batch sizes along with Gaussian Mixture Model (GMM) to alleviate the impact of noise and potential clustering errors, especially in privacy-sensitive scenarios. We provide theoretical analysis of the effectiveness of our proposed approach. We also extensively evaluate our approach across diverse data distributions and privacy budgets and show its effectiveness in mitigating the disparate impact of DP in FL settings with a small computational cost.
2025-10-06
TMLR (accepté)
doi.org
openreview.net
Say It Another Way: Auditing LLMs with a User-Grounded Automated Paraphrasing Framework
Cléa Chataigner
Rebecca Ma
Prakhar Ganesh
Afaf Taïk
Elliot Creager
Golnoosh Farnadi
2025-09-21
NeurIPS.cc/2025/Workshop/WiML (publié)
openreview.net
openreview.net
Multilingual Hallucination Gaps
Cléa Chataigner
Afaf Taïk
Golnoosh Farnadi
Large language models (LLMs) are increasingly used as alternatives to traditional searchengines given their capacity to generate text that r… (voir plus)esembles human language. However, thisshift is concerning, as LLMs often generate hallucinations—misleading or false informationthat appears highly credible. In this study, we explore the phenomenon of hallucinationsacross multiple languages in free-form text generation, focusing on what we call multilingualhallucination gaps. These gaps reflect differences in the frequency of hallucinated answersdepending on the prompt and language used. To quantify such hallucinations, we used theFActScore metric and extended its framework to a multilingual setting. We conductedexperiments using LLMs from the LLaMA, Qwen, and Aya families, generating biographiesin 19 languages and comparing the results to Wikipedia pages. Our results reveal varia-tions in hallucination rates, especially between high- and low-resource languages, raisingimportant questions about LLM multilingual performance and the challenges in evaluatinghallucinations in multilingual free-form text generation.
2025-04-22
Proceedings of the Algorithmic Fairness Through the Lens of Metrics and Evaluation (publié)
proceedings.mlr.press
proceedings.mlr.press
Systemizing Multiplicity: The Curious Case of Arbitrariness in Machine Learning
Prakhar Ganesh
Afaf Taïk
Golnoosh Farnadi
Algorithmic modeling relies on limited information in data to extrapolate outcomes for unseen scenarios, often embedding an element of arbit… (voir plus)rariness in its decisions. A perspective on this arbitrariness that has recently gained interest is multiplicity-the study of arbitrariness across a set of "good models", i.e., those likely to be deployed in practice. In this work, we systemize the literature on multiplicity by: (a) formalizing the terminology around model design choices and their contribution to arbitrariness, (b) expanding the definition of multiplicity to incorporate underrepresented forms beyond just predictions and explanations, (c) clarifying the distinction between multiplicity and other lenses of arbitrariness, i.e., uncertainty and variance, and (d) distilling the benefits and potential risks of multiplicity into overarching trends, situating it within the broader landscape of responsible AI. We conclude by identifying open research questions and highlighting emerging trends in this young but rapidly growing area of research.
2025-01-23
ArXiv (prépublication)
doi.org
arxiv.org
Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy
Khaoula Chehbouni
Martine De Cock
Gilles Caporossi
Afaf Taïk
Reihaneh Rabbany
Golnoosh Farnadi
The increased screen time and isolation caused by the COVID-19 pandemic have led to a significant surge in cases of online grooming, which i… (voir plus)s the use of strategies by predators to lure children into sexual exploitation. Previous efforts to detect grooming in industry and academia have involved accessing and monitoring private conversations through centrally-trained models or sending private conversations to a global server. In this work, we implement a privacy-preserving pipeline for the early detection of sexual predators. We leverage federated learning and differential privacy in order to create safer online spaces for children while respecting their privacy. We investigate various privacy-preserving implementations and discuss their benefits and shortcomings. Our extensive evaluation using real-world data proves that privacy and utility can coexist with only a slight reduction in utility.
2025-01-20
ArXiv (prépublication)
doi.org
arxiv.org
Balancing Profit and Fairness in Risk-Based Pricing Markets
Jesse Thibodeau
Hadi Nekoei
Afaf Taïk
Janarthanan Rajendran
Golnoosh Farnadi
Dynamic, risk-based pricing can systematically exclude vulnerable consumer groups from essential resources such as health insurance and cons… (voir plus)umer credit. We show that a regulator can realign private incentives with social objectives through a learned, interpretable tax schedule. First, we provide a formal proposition that bounding each firm's \emph{local} demographic gap implicitly bounds the \emph{global} opt-out disparity, motivating firm-level penalties. Building on this insight we introduce \texttt{MarketSim} -- an open-source, scalable simulator of heterogeneous consumers and profit-maximizing firms -- and train a reinforcement learning (RL) social planner (SP) that selects a bracketed fairness-tax while remaining close to a simple linear prior via an
2024-12-31
arXiv (prépublication)
doi.org
arxiv.org
Multilingual Hallucination Gaps in Large Language Models
Cl'ea Chataigner
Afaf Taïk
Golnoosh Farnadi
Large language models (LLMs) are increasingly used as alternatives to traditional search engines given their capacity to generate text that … (voir plus)resembles human language. However, this shift is concerning, as LLMs often generate hallucinations, misleading or false information that appears highly credible. In this study, we explore the phenomenon of hallucinations across multiple languages in freeform text generation, focusing on what we call multilingual hallucination gaps. These gaps reflect differences in the frequency of hallucinated answers depending on the prompt and language used. To quantify such hallucinations, we used the FactScore metric and extended its framework to a multilingual setting. We conducted experiments using LLMs from the LLaMA, Qwen, and Aya families, generating biographies in 19 languages and comparing the results to Wikipedia pages. Our results reveal variations in hallucination rates, especially between high and low resource languages, raising important questions about LLM multilingual performance and the challenges in evaluating hallucinations in multilingual freeform text generation.
2024-10-22
ArXiv (prépublication)
doi.org
arxiv.org
From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards
Khaoula Chehbouni
Megha Roshan
Emmanuel Ma
Futian Andrew Wei
Afaf Taïk
Jackie CK Cheung
Golnoosh Farnadi
2024-07-31
Findings of the Association for Computational Linguistics ACL 2024 (publié)
doi.org
arxiv.org
Fairness Incentives in Response to Unfair Dynamic Pricing
Jesse Thibodeau
Hadi Nekoei
Afaf Taïk
Janarthanan Rajendran
Golnoosh Farnadi
The use of dynamic pricing by profit-maximizing firms gives rise to demand fairness concerns, measured by discrepancies in consumer groups' … (voir plus)demand responses to a given pricing strategy. Notably, dynamic pricing may result in buyer distributions unreflective of those of the underlying population, which can be problematic in markets where fair representation is socially desirable. To address this, policy makers might leverage tools such as taxation and subsidy to adapt policy mechanisms dependent upon their social objective. In this paper, we explore the potential for AI methods to assist such intervention strategies. To this end, we design a basic simulated economy, wherein we introduce a dynamic social planner (SP) to generate corporate taxation schedules geared to incentivizing firms towards adopting fair pricing behaviours, and to use the collected tax budget to subsidize consumption among underrepresented groups. To cover a range of possible policy scenarios, we formulate our social planner's learning problem as a multi-armed bandit, a contextual bandit and finally as a full reinforcement learning (RL) problem, evaluating welfare outcomes from each case. To alleviate the difficulty in retaining meaningful tax rates that apply to less frequently occurring brackets, we introduce FairReplayBuffer, which ensures that our RL agent samples experiences uniformly across a discretized fairness space. We find that, upon deploying a learned tax and redistribution policy, social welfare improves on that of the fairness-agnostic baseline, and approaches that of the analytically optimal fairness-aware baseline for the multi-armed and contextual bandit settings, and surpassing it by 13.19% in the full RL setting.
2024-04-21
ArXiv (prépublication)
doi.org
arxiv.org
Promoting Fair Vaccination Strategies Through Influence Maximization: A Case Study on COVID-19 Spread
Nicola Neophytou
Afaf Taïk
Golnoosh Farnadi
The aftermath of the Covid-19 pandemic saw more severe outcomes for racial minority groups and economically-deprived communities. Such dispa… (voir plus)rities can be explained by several factors, including unequal access to healthcare, as well as the inability of low income groups to reduce their mobility due to work or social obligations. Moreover, senior citizens were found to be more susceptible to severe symptoms, largely due to age-related health reasons. Adapting vaccine distribution strategies to consider a range of demographics is therefore essential to address these disparities. In this study, we propose a novel approach that utilizes influence maximization (IM) on mobility networks to develop vaccination strategies which incorporate demographic fairness. By considering factors such as race, social status, age, and associated risk factors, we aim to optimize vaccine distribution to achieve various fairness definitions for one or more protected attributes at a time. Through extensive experiments conducted on Covid-19 spread in three major metropolitan areas across the United States, we demonstrate the effectiveness of our proposed approach in reducing disease transmission and promoting fairness in vaccination distribution.
2024-03-23
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements
Maryam Molamohammadi
Afaf Taïk
Nicolas Le Roux
Golnoosh Farnadi
The growing utilization of machine learning (ML) in decision-making processes raises questions about its benefits to society. In this study,… (voir plus) we identify and analyze three axes of heterogeneity that significantly influence the trajectory of ML products. These axes are i) values, culture and regulations, ii) data composition, and iii) resource and infrastructure capacity. We demonstrate how these axes are interdependent and mutually influence one another, emphasizing the need to consider and address them jointly. Unfortunately, the current research landscape falls short in this regard, often failing to adopt a holistic approach. We examine the prevalent practices and methodologies that skew these axes in favor of a selected few, resulting in power concentration, homogenized control, and increased dependency. We discuss how this fragmented study of the three axes poses a significant challenge, leading to an impractical solution space that lacks reflection of real-world scenarios. Addressing these issues is crucial to ensure a more comprehensive understanding of the interconnected nature of society and to foster the democratic and inclusive development of ML systems that are more aligned with real-world complexities and its diverse requirements.
2023-10-28
Equity and Access in Algorithms, Mechanisms, and Optimization (publié)
doi.org
arxiv.org