A Model Ensemble-Based Post-Processing Framework for Fairness-Aware Prediction | ScienceToStartup | ScienceToStartup

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References (53)

[1]

Post-processing fairness with minimal changes

2024Federico Di Gennaro, Thibault Laugel et al.

[2]

FairMOE: counterfactually-fair mixture of experts with levels of interpretability

2024Joe Germino, Nuno Moniz et al.

[3]

FRAPPÉ: A Group Fairness Framework for Post-Processing Everything

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"A post-processing framework that enhances fairness in machine learning predictions without altering model internals, applicable across diverse tasks and fairness definitions."

Fairness in ML•Score: 5•View PDF ↗

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A Model Ensemble-Based Post-Processing Framework for Fairness-Aware Prediction

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References (53)

Founder's Pitch

"A post-processing framework that enhances fairness in machine learning predictions without altering model internals, applicable across diverse tasks and fairness definitions."

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