We review and reflect on fairness notions proposed in machine learning literature and make
an attempt to draw connections to arguments in moral and political philosophy, especially …

Artificial intelligence (AI) has been advancing at a fast pace and it is now poised for
deployment in a wide range of applications, such as autonomous systems, medical …

In the past decade, deep learning became the prevalent methodology for predictive
modeling thanks to the remarkable accuracy of deep neural networks in tasks such as …

Providing formal guarantees of algorithmic fairness is of paramount importance to socially
responsible deployment of machine learning algorithms. In this work, we study formal …

Post-hoc explanation methods are used with the intent of providing insights about neural
networks and are sometimes said to help engender trust in their outputs. However, popular …

We consider the problem of whether a Neural Network (NN) model satisfies global individual
fairness. Individual Fairness (defined in (Dwork et al. 2012)) suggests that similar individuals …

We present REGLO, a novel methodology for repairing pretrained neural networks to satisfy
global robustness and individual fairness properties. A neural network is said to be globally …

In this paper, we introduce BNN-DP, an efficient algorithmic framework for analysis of
adversarial robustness of Bayesian Neural Networks (BNNs). Given a compact set of input …

As responsible AI gains importance in machine learning algorithms, properties like fairness,
adversarial robustness, and causality have received considerable attention in recent years …

Fairness in machine learning (ML) has received much attention. However, existing studies
have mainly focused on the distributive fairness of ML models. The other dimension of …