Mohammad Yaghini

[Mohammad Yaghini @MYaghini]

The privacy-fairness tension is considerably weaker than previously believed. It appears largely to be an artifact of algorithm design.

🌐 https://t.co/w632kACccA
📄 https://t.co/H3hPUzEfB7
💻 https://t.co/HMoTigYvWI

[Mohammad Yaghini @MYaghini]

Can ML models be trained under fairness constraints with formal DP guarantees, without sacrificing utility?
New paper at ICLR 2026: RaCO-DP
Poster: Pavilion 4 (later today!)
🧵
(w/ @tudorcebere, Michael Menart, Aurélien Bellet, @NicolasPapernot )

[Mohammad Yaghini @MYaghini]

Unlike regularization-based methods, RaCO-DP enforces bounds directly by construction: no hyperparameter tuning, no extra privacy cost.
Also scales to many simultaneous constraints without degradation, and runs three orders of magnitude faster than the prior SOTA, DP-FERMI.

[Ali Shahin Shamsabadi @AliShahinShams1]

Curious about novel risks that AI introduces for patients and healthcare providers?
We're hosting an #AIUK Fringe event to explore Privacy and Fairness in AI for Health – Register now!
https://t.co/oUSY5wgqsC
Date: 27 March, 10:00-16:30 GMT
Location: @turinginst, London
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[Mohammad Yaghini @MYaghini]

@arkrause Congratulations Andreas!

[Mohammad Yaghini @MYaghini]

@HodaHeidari @mldcmu @isrcmu Congratulations Hoda.

[Mohammad Yaghini @MYaghini]

@hiddenmarkov @Aaroth Moreover, we show, via a probabilistic formulation, how the main cause of disparity is two-fold: under/over representation and model generalization differences across subgroups.