4 citations · 8 across the 9 of their papers we have counts for
5 papers · 1 filter
Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations
Yehonatan Elisha, Seffi Cohen, Oren Barkan +1
Saliency maps are widely used for visual explanations in deep learning, but a fundamental lack of consensus persists regarding their intended purpose and alignment with diverse use…
DFPE: A Diverse Fingerprint Ensemble for Enhancing LLM Performance
Seffi Cohen, Niv Goldshlager, Nurit Cohen-Inger +2
Large Language Models (LLMs) have shown remarkable capabilities across various natural language processing tasks but often struggle to excel uniformly in diverse or complex domains…
Forget What You Know about LLMs Evaluations -- LLMs are Like a Chameleon
Nurit Cohen-Inger, Yehonatan Elisha, Bracha Shapira +2
Large language models (LLMs) often appear to excel on public benchmarks, but these high scores may mask an overreliance on dataset-specific surface cues rather than true language u…
FairTTTS: A Tree Test Time Simulation Method for Fairness-Aware Classification
Nurit Cohen-Inger, Lior Rokach, Bracha Shapira +1
Algorithmic decision-making has become deeply ingrained in many domains, yet biases in machine learning models can still produce discriminatory outcomes, often harming unprivileged…
BiasGuard: Guardrailing Fairness in Machine Learning Production Systems
Nurit Cohen-Inger, Seffi Cohen, Neomi Rabaev +2
As machine learning (ML) systems increasingly impact critical sectors such as hiring, financial risk assessments, and criminal justice, the imperative to ensure fairness has intens…