SOTAVerified

Explainable artificial intelligence

XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–175 of 971 papers

TitleStatusHype
ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques—0
Argumentation Theoretical Frameworks for Explainable Artificial Intelligence—0
A Meta Survey of Quality Evaluation Criteria in Explanation Methods—0
Additive-feature-attribution methods: a review on explainable artificial intelligence for fluid dynamics and heat transfer—0
Argumentation-based Agents that Explain their Decisions—0
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?—0
A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making—0
A Means-End Account of Explainable Artificial Intelligence—0
A Review of Explainable Artificial Intelligence in Manufacturing—0
A Data-Driven Framework for Identifying Investment Opportunities in Private Equity—0
A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life—0
Concept Induction using LLMs: a user experiment for assessment—0
Are Large Language Models the New Interface for Data Pipelines?—0
Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systems—0
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models—0
Challenges for cognitive decoding using deep learning methods—0
A Psychological Theory of Explainability—0
Challenges and Opportunities in Text Generation Explainability—0
A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method—0
A Data-Driven Exploration of Elevation Cues in HRTFs: An Explainable AI Perspective Across Multiple Datasets—0
A Practical guide on Explainable AI Techniques applied on Biomedical use case applications—0
ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Text—0
Are Linear Regression Models White Box and Interpretable?—0
Clash of the Explainers: Argumentation for Context-Appropriate Explanations—0
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning—0
Show:102550
← PrevPage 7 of 39Next →

No leaderboard results yet.