ManyFairHPO: A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes

Posted on October 21, 2024 by Noor Awad

arXiv Fairness in AI is a complex and evolving challenge that continues to spark intense debate and research in the tech community. As we grapple with the ethical implications of AI decision-making, we proposed a new approach called ManyFairHPO, offering a fresh perspective on this critical issue. While not a silver bullet, this innovative framework […]

Can Fairness be Automated?

Posted on April 3, 2023 by Noor Awad, Hilde Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Eddie Bergman, Mykola Pechenizkiy, Joaquin Vanschoren, Bernd Bischl, Frank Hutter

At the risk of sounding cliché, “with great power comes great responsibility.” While we don’t want to suggest that machine learning (ML) practitioners are superheroes, what was true for Spiderman is also true for those building predictive models – and even more so for those building AutoML tools. Only last year, the Netherlands Institute for […]

DEHB

Posted on August 10, 2022 by Noor Awad

DEHB: EVOLUTIONARY HYPERBAND FOR SCALABLE, ROBUST AND EFFICIENT HYPERPARAMETER OPTIMIZATION By Noor Awad, Modern machine learning algorithms crucially rely on several design decisions to achieve strong performance, making the problem of Hyperparameter Optimization (HPO) more important than ever. We believe that a practical, general HPO method must fulfill many desiderata, including: (1) strong anytime performance, […]

ManyFairHPO: A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes

Can Fairness be Automated?

DEHB

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