2 citations · 2 across the 1 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
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…