1 citations · 3 across the 9 of their papers we have counts for
3 papers · 1 filter
On the Origins of Sampling Bias: Implications on Fairness Measurement and Mitigation
Sami Zhioua, Ruta Binkyte, Ayoub Ouni +1
Accurately measuring discrimination is crucial to faithfully assessing fairness of trained machine learning (ML) models. Any bias in measuring discrimination leads to either amplif…
Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models
Ruta Binkyte, Ivaxi Sheth, Zhijing Jin +3
Ensuring trustworthiness in machine learning (ML) systems is crucial as they become increasingly embedded in high-stakes domains. This paper advocates for integrating causal method…
BaBE: Enhancing Fairness via Estimation of Latent Explaining Variables
Ruta Binkyte, Daniele Gorla, Catuscia Palamidessi
We consider the problem of unfair discrimination between two groups and propose a pre-processing method to achieve fairness. Corrective methods like statistical parity usually lead…