In this work, we present Fairness Aware Counterfactuals for Subgroups (FACTS), a
framework for auditing subgroup fairness through counterfactual explanations. We start with …

Algorithmic fairness and explainability are foundational elements for achieving responsible
AI. In this paper, we focus on their interplay, a research area that is recently receiving …

Graph neural networks (GNNs) find applications in various domains such as computational
biology, natural language processing, and computer security. Owing to their popularity, there …

The widespread deployment of machine learning systems in critical real-world decision-
making applications has highlighted the urgent need for counterfactual explainability …

Graph Neural Networks (GNNs) have been widely deployed in various real-world
applications. However, most GNNs are black-box models that lack explanations. One …

Personalized Federal learning (PFL) allows clients to cooperatively train a personalized
model without disclosing their private dataset. However, PFL suffers from Non-IID …

This paper introduces the first graph-based framework for generating group counterfactual
explanations to audit model fairness, a crucial aspect of trustworthy machine learning …

Counterfactual explanations (CE) identify data points that closely resemble the observed
data but produce different machine learning (ML) model outputs, offering critical insights into …

Growing regulatory and societal pressures demand increased transparency in AI,
particularly in understanding the decisions made by complex machine learning models …

This chapter discusses the ethical complications and challenges arising from the use of AI
systems in our everyday lives. It outlines recent and upcoming regulations and policies …