483 citations · 507 across the 11 of their papers we have counts for
17 papers
Deontic Policies for Runtime Governance of Agentic AI Systems
Anupam Joshi, Tim Finin, Karuna Pande Joshi +1
Autonomous agentic AI systems driven by Large Language Models (LLMs) introduce a new class of security, privacy, and compliance challenges: an agent that can invoke tools, manipula…
Parameter Efficiency Is Not Memory Efficiency: Rethinking Fine-Tuning for On-Device LLM Adaptation
Irene Tenison, Stella Ahn, Miriam Kim +2
Parameter-Efficient Fine-Tuning (PEFT) has become the standard for adapting large language models (LLMs). In this work we challenge the wide-spread assumption that parameter effici…
Learning Concept Bottleneck Models from Mechanistic Explanations
Antonio De Santis, Schrasing Tong, Marco Brambilla +1
Concept Bottleneck Models (CBMs) aim for ante-hoc interpretability by learning a bottleneck layer that predicts interpretable concepts before the decision. State-of-the-art approac…
Mitigating Bias in Concept Bottleneck Models for Fair and Interpretable Image Classification
Schrasing Tong, Antoine Salaun, Vincent Yuan +2
Ensuring fairness in image classification prevents models from perpetuating and amplifying bias. Concept bottleneck models (CBMs) map images to high-level, human-interpretable conc…
Measuring Perceptions of Fairness in AI Systems: The Effects of Infra-marginality
Schrasing Tong, Minseok Jung, Ilaria Liccardi +1
Differences in data distributions between demographic groups, known as the problem of infra-marginality, complicate how people evaluate fairness in machine learning models. We pres…
Forget to Generalize: Iterative Adaptation for Generalization in Federated Learning
Abdulrahman Alotaibi, Irene Tenison, Miriam Kim +2
The Web is naturally heterogeneous with user devices, geographic regions, browsing patterns, and contexts all leading to highly diverse, unique datasets. Federated Learning (FL) is…