251 citations · 264 across the 17 of their papers we have counts for
17 papers
CIRCLE: A Framework for Evaluating AI from a Real-World Lens
Reva Schwartz, Carina Westling, Morgan Briggs +12
This study proposes CIRCLE, a six-stage, lifecycle-based framework to bridge the reality gap between model-centric performance metrics and AI system outcomes in deployment. Current…
Balancing Profit and Fairness in Risk-Based Pricing Markets
Jesse Thibodeau, Hadi Nekoei, Afaf Taïk +2
Dynamic, risk-based pricing can systematically exclude vulnerable consumer groups from essential resources such as health insurance and consumer credit. We show that a regulator ca…
Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects
Reva Schwartz, Rumman Chowdhury, Akash Kundu +17
Conventional AI evaluation approaches concentrated within the AI stack exhibit systemic limitations for exploring, navigating and resolving the human and societal factors that play…
Fairness in Federated Learning: Fairness for Whom?
Afaf Taik, Khaoula Chehbouni, Golnoosh Farnadi
Fairness in federated learning has emerged as a rapidly growing area of research, with numerous works proposing formal definitions and algorithmic interventions. Yet, despite this…
Say It Another Way: Auditing LLMs with a User-Grounded Automated Paraphrasing Framework
Cléa Chataigner, Rebecca Ma, Prakhar Ganesh +4
Large language models (LLMs) are highly sensitive to subtle changes in prompt phrasing, posing challenges for reliable auditing. Prior methods often apply unconstrained prompt para…
Systemizing Multiplicity: The Curious Case of Arbitrariness in Machine Learning
Prakhar Ganesh, Afaf Taik, Golnoosh Farnadi
Algorithmic modeling relies on limited information in data to extrapolate outcomes for unseen scenarios, often embedding an element of arbitrariness in its decisions. A perspective…
Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy
Khaoula Chehbouni, Martine De Cock, Gilles Caporossi +3
The increased screen time and isolation caused by the COVID-19 pandemic have led to a significant surge in cases of online grooming, which is the use of strategies by predators to…
Multilingual Hallucination Gaps in Large Language Models
Cléa 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 resembles human language. However, this…
Differentially Private Clustered Federated Learning
Saber Malekmohammadi, Afaf Taik, Golnoosh Farnadi
Federated learning (FL), which is a decentralized machine learning (ML) approach, often incorporates differential privacy (DP) to provide rigorous data privacy guarantees. Previous…
Fairness Incentives in Response to Unfair Dynamic Pricing
Jesse Thibodeau, Hadi Nekoei, Afaf Taïk +2
The use of dynamic pricing by profit-maximizing firms gives rise to demand fairness concerns, measured by discrepancies in consumer groups' demand responses to a given pricing stra…
From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards
Khaoula Chehbouni, Megha Roshan, Emmanuel Ma +4
Recent progress in large language models (LLMs) has led to their widespread adoption in various domains. However, these advancements have also introduced additional safety risks an…
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 disparities can be explained by several facto…
Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements
Maryam Molamohammadi, Afaf Taik, Nicolas Le Roux +1
The growing utilization of machine learning (ML) in decision-making processes raises questions about its benefits to society. In this study, we identify and analyze three axes of h…
Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networks
Amine Abouaomar, Afaf Taik, Abderrahime Filali +1
Radio access network (RAN) slicing is a key element in enabling current 5G networks and next-generation networks to meet the requirements of different services in various verticals…
Clustered Vehicular Federated Learning: Process and Optimization
Afaf Taik, Zoubeir Mlika, Soumaya Cherkaoui
Federated Learning (FL) is expected to play a prominent role for privacy-preserving machine learning (ML) in autonomous vehicles. FL involves the collaborative training of a single…
Electrical Load Forecasting Using Edge Computing and Federated Learning
Afaf Taik, Soumaya Cherkaoui
In the smart grid, huge amounts of consumption data are used to train deep learning models for applications such as load monitoring and demand response. However, these applications…
Federated Edge Learning : Design Issues and Challenges
Afaf Taïk, Soumaya Cherkaoui
Federated Learning (FL) is a distributed machine learning technique, where each device contributes to the learning model by independently computing the gradient based on its local…