Moninder Singh

TL;DR: A new open source Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360), released under an Apache v2.0 license to help facilitate the transition of fairness research algorithms to use in an industrial setting and to provide a common framework for fairness researchers to share and evaluate algorithms.

TL;DR: A new open-source Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360), released under an Apache v2.0 license, to help facilitate the transition of fairness research algorithms for use in an industrial setting and to provide a common framework for fairness researchers to share and evaluate algorithms.

TL;DR: This work introduces AI Explainability 360, an open-source software toolkit featuring eight diverse and state-of-the-art explainability methods and two evaluation metrics, and provides a taxonomy to help entities requiring explanations to navigate the space of explanation methods.

TL;DR: In this article, the authors present a method to enforce privacy preferences on exchanges of personal data of a data-subject, which comprises the steps of specifying data subject authorization rule sets having subject constraints, receiving a request message from a requester and a requesters privacy statement, comparing the requester privacy statement to the subject constraints and releasing the data subject data in a response message to the requesters only if the subject constraint are satisfied.

TL;DR: This paper proposes a new framework for data protection that is built on the foundation of privacy and security technologies, and provides secure environments for protected execution, which is essential to limiting data access to specific purposes.