no code implementations • 28 Nov 2023 • Dave Mbiazi, Meghana Bhange, Maryam Babaei, Ivaxi Sheth, Patrik Joslin Kenfack
The past decade has observed a great advancement in AI with deep learning-based models being deployed in diverse scenarios including safety-critical applications.
1 code implementation • 24 Jul 2023 • Patrik Joslin Kenfack, Samira Ebrahimi Kahou, Ulrich Aïvodji
Surprisingly, our framework outperforms models trained with constraints on the true sensitive attributes.
no code implementations • 13 Jul 2022 • Patrik Joslin Kenfack, Kamil Sabbagh, Adín Ramírez Rivera, Adil Khan
Fairness has become an essential problem in many domains of Machine Learning (ML), such as classification, natural language processing, and Generative Adversarial Networks (GANs).
no code implementations • 27 Jul 2021 • Patrik Joslin Kenfack, Adil Mehmood Khan, Rasheed Hussain, S. M. Ahsan Kazmi
Training machine learning models with the only accuracy as a final goal may promote prejudices and discriminatory behaviors embedded in the data.
1 code implementation • 22 Jun 2021 • Archit Uniyal, Rakshit Naidu, Sasikanth Kotti, Sahib Singh, Patrik Joslin Kenfack, FatemehSadat Mireshghallah, Andrew Trask
Recent advances in differentially private deep learning have demonstrated that application of differential privacy, specifically the DP-SGD algorithm, has a disparate impact on different sub-groups in the population, which leads to a significantly high drop-in model utility for sub-populations that are under-represented (minorities), compared to well-represented ones.
no code implementations • 1 Mar 2021 • Patrik Joslin Kenfack, Daniil Dmitrievich Arapov, Rasheed Hussain, S. M. Ahsan Kazmi, Adil Mehmood Khan
Generative adversarial networks (GANs) are one of the greatest advances in AI in recent years.