most citedIs Trust Correlated With Explainability in AI? A Meta-Analysis

31 citations · 33 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2025

ScenarioBench: Trace-Grounded Compliance Evaluation for Text-to-SQL and RAG

Zahra Atf, Peter R Lewis

ScenarioBench is a policy-grounded, trace-aware benchmark for evaluating Text-to-SQL and retrieval-augmented generation in compliance contexts. Each YAML scenario includes a no-pee…

cs.CL2025

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation

Zahra Atf, Peter R Lewis

Large language models (LLMs) are increasingly used in high-stakes settings, where explaining uncertainty is both technical and ethical. Probabilistic methods are often opaque and m…

cs.CY20251 cited

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs

Nariman Naderi, Zahra Atf, Peter R Lewis +3

This paper investigates how prompt engineering techniques impact both accuracy and confidence elicitation in Large Language Models (LLMs) applied to medical contexts. Using a strat…

cs.AI202531 cited

Is Trust Correlated With Explainability in AI? A Meta-Analysis

Zahra Atf, Peter R. Lewis

This study critically examines the commonly held assumption that explicability in artificial intelligence (AI) systems inherently boosts user trust. Utilizing a meta-analytical app…

cs.AI20251 cited

The challenge of uncertainty quantification of large language models in medicine

Zahra Atf, Seyed Amir Ahmad Safavi-Naini, Peter R. Lewis +4

This study investigates uncertainty quantification in large language models (LLMs) for medical applications, emphasizing both technical innovations and philosophical implications.…