483 citations · 670 across the 17 of their papers we have counts for
18 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…
Activation-Efficient Fine-Tuning via Sequence-Compressed Low-Rank Subspaces
Irene Tenison, Stella Ahn, Miriam Kim +2
Parameter-Efficient Fine-Tuning (PEFT) has become the standard approach for adapting large language models (LLMs). However, despite reducing trainable parameter counts, many PEFT m…
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…
Investigating Model Editing for Unlearning in Large Language Models
Shariqah Hossain, Lalana Kagal
Machine unlearning aims to remove unwanted information from a model, but many methods are inefficient for LLMs with large numbers of parameters or fail to fully remove the intended…
FTTE: Enabling Federated and Resource-Constrained Deep Edge Intelligence
Irene Tenison, Anna Murphy, Charles Beauville +1
Federated learning (FL) enables collaborative model training across distributed devices while preserving data privacy, but deployment on resource-constrained edge nodes remains cha…
Towards Resource Efficient and Interpretable Bias Mitigation in Large Language Models
Schrasing Tong, Eliott Zemour, Jessica Lu +2
Although large language models (LLMs) have demonstrated their effectiveness in a wide range of applications, they have also been observed to perpetuate unwanted biases present in t…
Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners
Vaikkunth Mugunthan, Pawan Goyal, Lalana Kagal
Vertical Federated Learning (VFL) refers to the collaborative training of a model on a dataset where the features of the dataset are split among multiple data owners, while label i…
Investigating Bias in Image Classification using Model Explanations
Schrasing Tong, Lalana Kagal
We evaluated whether model explanations could efficiently detect bias in image classification by highlighting discriminating features, thereby removing the reliance on sensitive at…
DPD-InfoGAN: Differentially Private Distributed InfoGAN
Vaikkunth Mugunthan, Vignesh Gokul, Lalana Kagal +1
Generative Adversarial Networks (GANs) are deep learning architectures capable of generating synthetic datasets. Despite producing high-quality synthetic images, the default GAN ha…
BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning
Vaikkunth Mugunthan, Ravi Rahman, Lalana Kagal
Federated learning enables the development of a machine learning model among collaborating agents without requiring them to share their underlying data. However, malicious agents w…
PrivacyFL: A simulator for privacy-preserving and secure federated learning
Vaikkunth Mugunthan, Anton Peraire-Bueno, Lalana Kagal
Federated learning is a technique that enables distributed clients to collaboratively learn a shared machine learning model while keeping their training data localized. This reduce…
Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence
Midas Nouwens, Ilaria Liccardi, Michael Veale +2
New consent management platforms (CMPs) have been introduced to the web to conform with the EU's General Data Protection Regulation, particularly its requirements for consent when…
Explaining Explanations: An Overview of Interpretability of Machine Learning
Leilani H. Gilpin, David Bau, Ben Z. Yuan +3
There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often ca…
Enforceable Data Sharing Agreements Using Smart Contracts
Kevin Liu, Harsh Desai, Lalana Kagal +1
As more and more data is collected for various reasons, the sharing of such data becomes paramount to increasing its value. Many applications ranging from smart cities to personali…
Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box Models
Julius Adebayo, Lalana Kagal
Predictive models are increasingly deployed for the purpose of determining access to services such as credit, insurance, and employment. Despite potential gains in productivity and…