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Evaluating LLMs for Gender Disparities in Notable Persons
This study examines the use of Large Language Models (LLMs) for retrieving factual information, addressing concerns over their propensity to produce factually incorrect "hallucinated" responses or to altogether decline to even answer prompt at all.
Beyond Accuracy-Fairness: Stop evaluating bias mitigation methods solely on between-group metrics
Artificial Intelligence (AI) finds widespread applications across various domains, sparking concerns about fairness in its deployment.
Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem
Artificial Intelligence (AI) systems are increasingly used in high-stakes domains of our life, increasing the need to explain these decisions and to make sure that they are aligned with how we want the decision to be made.
Unveiling the Potential of Counterfactuals Explanations in Employability
In eXplainable Artificial Intelligence (XAI), counterfactual explanations are known to give simple, short, and comprehensible justifications for complex model decisions.
The privacy issue of counterfactual explanations: explanation linkage attacks
Black-box machine learning models are being used in more and more high-stakes domains, which creates a growing need for Explainable AI (XAI).
