1 citations · 1 across the 4 of their papers we have counts for
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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…
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…
On the Need and Applicability of Causality for Fairness: A Unified Framework for AI Auditing and Legal Analysis
Ruta Binkyte, Ljupcho Grozdanovski, Sami Zhioua
As Artificial Intelligence (AI) increasingly influences decisions in critical societal sectors, understanding and establishing causality becomes essential for evaluating the fairne…