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Notes on explainability
7 notes
Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information
The paper presents a new method called 'Rule of Thumb' (RoT) for explaining how Artificial Intelligence systems make decisions. RoT identifies the most relevant features of an AI system to predict its behavior for a…
Challenges in Evaluating Explanation Methods for Static and Evolving Data
The paper discusses the limitations in evaluating Explainable Artificial Intelligence (XAI) systems, specifically focusing on the DetoxAI image recognition system. It highlights the need for better evaluation methods…
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
Researchers have proposed a self-evolving framework to improve the quality of depression symptom annotations in AI systems for mental health research. The framework combines large language models with human expertise…
Explaining Reinforcement Learning Agents via Inductive Logic Programming
This research aims to improve Reinforcement Learning (RL) policies by making them more transparent and interpretable. It proposes objective and planning-oriented metrics for policy explainability in RL settings using…
Evaluating RE Practices for Explainability: Synthesizing Insights from Daimler Truck into an Explainable RE Framework Proposal
A study investigates the limitations of current Requirements Engineering (RE) practices in supporting explainability requirements, particularly within industrial contexts. The research focuses on Daimler Truck and…
Evaluating RE Practices for Explainability: Synthesizing Insights from Daimler Truck into an Explainable RE Framework Proposal
This research paper reports early findings from an ongoing study investigating how requirements engineering (RE) practices support explainability in AI-based systems, particularly in the industrial context of Daimler…
FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning
The paper introduces FedXDS, a method that uses explainable AI (XAI) to help mitigate performance degradation in federated learning caused by statistical heterogeneity between client data distributions. The approach…
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