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 776–800 of 971 papers

TitleStatusHype
Breaking Down Financial News Impact: A Novel AI Approach with Geometric Hypergraphs—0
Bridging Human Concepts and Computer Vision for Explainable Face Verification—0
BSED: Baseline Shapley-Based Explainable Detector—0
Multihop: Leveraging Complex Models to Learn Accurate Simple Models—0
CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures—0
CAManim: Animating end-to-end network activation maps—0
Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI—0
Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements—0
Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience—0
Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals—0
CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models—0
Causal Explanations and XAI—0
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning—0
Challenges and Opportunities in Text Generation Explainability—0
Challenges for cognitive decoding using deep learning methods—0
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models—0
ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Text—0
Clash of the Explainers: Argumentation for Context-Appropriate Explanations—0
Classification and Explanation of Distributed Denial-of-Service (DDoS) Attack Detection using Machine Learning and Shapley Additive Explanation (SHAP) Methods—0
Classification of Viral Pneumonia X-ray Images with the Aucmedi Framework—0
Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos—0
CNN-based explanation ensembling for dataset, representation and explanations evaluation—0
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME—0
Comparative Analysis of Hyperspectral Image Reconstruction Using Deep Learning for Agricultural and Biological Applications—0
Comparing interpretation methods in mental state decoding analyses with deep learning models—0
Show:102550
← PrevPage 32 of 39Next →

No leaderboard results yet.