Operationalizing Fairness: Post-Hoc Threshold Optimization Under Hard Resource Limits | ScienceToStartup | ScienceToStartup

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References (31)

[1]

Balancing Fairness and Accuracy in Machine Learning-Based Probability of Default Modeling via Threshold Optimization

2025Essodjolo Kpatcha

[2]

An Analysis of the New EU AI Act and A Proposed Standardization Framework for Machine Learning Fairness

2025M. Teodorescu, Yongxu Sun et al.

[3]

Learning for Sustainable Online Scheduling with Competitive Fairness Guarantees

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"A legal-compliant, model-agnostic ML fairness framework balancing safety, efficiency, and equity under strict resource limits."

Fairness in Machine Learning•Score: 6•View PDF ↗

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Operationalizing Fairness: Post-Hoc Threshold Optimization Under Hard Resource Limits

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References (31)

Founder's Pitch

"A legal-compliant, model-agnostic ML fairness framework balancing safety, efficiency, and equity under strict resource limits."

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Operationalizing Fairness: Post-Hoc Threshold Optimization Under Hard Resource Limits

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References (31)

Founder's Pitch

"A legal-compliant, model-agnostic ML fairness framework balancing safety, efficiency, and equity under strict resource limits."

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