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

Showing 5160 of 971 papers

TitleStatusHype
Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays0
Logic Explanation of AI Classifiers by Categorical Explaining Functors0
Explainable AI Components for Narrative Map ExtractionCode1
Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures0
Guarding Digital Privacy: Exploring User Profiling and Security Enhancements0
Securing Virtual Reality Experiences: Unveiling and Tackling Cybersickness Attacks with Explainable AI0
Computational identification of ketone metabolism as a key regulator of sleep stability and circadian dynamics via real-time metabolic profiling0
A Data-Driven Exploration of Elevation Cues in HRTFs: An Explainable AI Perspective Across Multiple Datasets0
GENEOnet: Statistical analysis supporting explainability and trustworthiness0
Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance0
Show:102550
← PrevPage 6 of 98Next →

No leaderboard results yet.