Search Results for author: Patrik Joslin Kenfack

Found 6 papers, 2 papers with code

Survey on AI Ethics: A Socio-technical Perspective

no code implementations28 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.

Ethics Fairness

Fairness Under Demographic Scarce Regime

1 code implementation24 Jul 2023 Patrik Joslin Kenfack, Samira Ebrahimi Kahou, Ulrich Aïvodji

Surprisingly, our framework outperforms models trained with constraints on the true sensitive attributes.

Attribute Fairness

RepFair-GAN: Mitigating Representation Bias in GANs Using Gradient Clipping

no code implementations13 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).

Fairness

Adversarial Stacked Auto-Encoders for Fair Representation Learning

no code implementations27 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.

Fairness Representation Learning

DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?

1 code implementation22 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.

Fairness

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