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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

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Showing 751–775 of 971 papers

TitleStatusHype
ProtoShotXAI: Using Prototypical Few-Shot Architecture for Explainable AICode0
Automated Quality Control of Vacuum Insulated Glazing by Convolutional Neural Network Image Classification—0
Explaining deep learning models for spoofing and deepfake detection with SHapley Additive exPlanationsCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
Classification of Viral Pneumonia X-ray Images with the Aucmedi Framework—0
Consistent Explanations by Contrastive LearningCode1
Advancing Nearest Neighbor Explanation-by-Example with Critical Classification Regions—0
Focus! Rating XAI Methods and Finding BiasesCode1
Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Work Through Explainable Artificial Intelligence—0
A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction—0
Multihop: Leveraging Complex Models to Learn Accurate Simple Models—0
When Stability meets Sufficiency: Informative Explanations that do not Overwhelm—0
Adherence and Constancy in LIME-RS Explanations for Recommendation—0
Knowledge-based XAI through CBR: There is more to explanations than models can tell—0
Longitudinal Distance: Towards Accountable Instance Attribution—0
Improvement of a Prediction Model for Heart Failure Survival through Explainable Artificial Intelligence—0
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey—0
CARE: Coherent Actionable Recourse based on Sound Counterfactual ExplanationsCode0
Challenges for cognitive decoding using deep learning methods—0
Logic Explained NetworksCode1
Interpretable Summaries of Black Box Incident Triaging with Subgroup DiscoveryCode0
Toward Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition SystemsCode1
On the Importance of Domain-specific Explanations in AI-based Cybersecurity Systems (Technical Report)—0
Towards explainable artificial intelligence (XAI) for early anticipation of traffic accidentsCode0
MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence—0
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