https://github.com/capitalone/ablation
공식 코드BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelligence
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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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelligence
Evaluating saliency methods on artificial data with different background types
Unveiling Molecular Moieties through Hierarchical Grad-CAM Graph Explainability
Explainable Artificial Intelligence and Multicollinearity : A Mini Review of Current Approaches
Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities
Shapley-based Explainable AI for Clustering Applications in Fault Diagnosis and Prognosis
Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature Spaces
Explainable Deep Learning Methods in Medical Image Classification: A Survey
Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistant
Intrinsic Subgraph Generation for Interpretable Graph based Visual Question Answering
Human-in-the-loop model explanation via verbatim boundary identification in generated neighborhoods
TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation
SLISEMAP: Supervised dimensionality reduction through local explanations
XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning
Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design
Improving Deep Neural Network Classification Confidence using Heatmap-based eXplainable AI
Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic Dataset
Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.