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Notes on explainable ai
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Topological Attribution Distance (TAD): Revealing Segment-Level RAG Influence on LLM Output Geometry for Incident Log Analysis
Researchers propose a method called Topological Attribution Distance (TAD) to help identify which parts of cyber incident logs are critical in influencing the outputs of Large Language Models (LLMs) used in…
Evaluating the Impact of Explainable AI on Trust in AI-Assisted Code Review
A study investigates how the level of explanation in AI-assisted code reviews affects developer trust and agreement with AI recommendations. The results show that full explanations lead to higher perceived trust but…
Evaluating the Impact of Explainable AI on Trust in AI-Assisted Code Review
A study investigates the impact of Explainable AI (XAI) on developers' trust in AI-assisted code reviews. The research compared three LLM-based code review systems with varying levels of XAI support. Results show that…
Explaining Process Control Optimisation Recommendations via GradientSHAP and Implicit Differentiation
Researchers propose a method to explain optimisation recommendations for industrial processes using Shapley Additive exPlanations (SHAP), Implicit Function Theorem, and Large Language Models (LLM). The new approach…
Explaining Process Control Optimisation Recommendations via GradientSHAP and Implicit Differentiation
Researchers have developed a method to explain optimization recommendations for industrial processes using GradientSHAP and Implicit Differentiation. This approach aims to bridge the trust gap between engineers who…
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
Researchers propose a self-evolving annotation framework for Major Depressive Disorder (MDD) that combines large language model (LLM)-assisted labeling with expert verification. The goal is to create explainable…
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
Researchers have proposed a new framework to improve the quality of annotations in depression-related datasets for artificial intelligence systems in mental health research. The framework uses a large language model…
Scientific Explanations in Health Sciences: Causality, Trust, and Epistemic Adequacy
This research paper discusses the need for better understanding and transparency in Artificial Intelligence (AI) used in healthcare, focusing on the explanatory process of Machine Learning models. It highlights that…
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