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.
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/fanconic/this-does-not-look-like-that
공식 코드https://github.com/fau-is/gam_comparison
공식 코드https://github.com/fau-is/xnap
공식 코드XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP
https://github.com/fawazsammani/nlxgpt
공식 코드NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks
https://github.com/faysal-md/an-interpretable-deep-learning-approach-for-skin-cancer-categorization-ieee2023
공식 코드An Interpretable Deep Learning Approach for Skin Cancer Categorization
https://github.com/ffattr/ffa
공식 코드https://github.com/flaat/llm-graph-cf
공식 코드Natural Language Counterfactual Explanations for Graphs Using Large Language Models
https://github.com/frederikpahde/medical-ai-safety
공식 코드https://github.com/fudong03/biointelligence
공식 코드https://github.com/fy-meng/cohex
공식 코드https://github.com/gabrielkasmi/robust_pv_mapping
공식 코드https://github.com/geemi725/xpertai
공식 코드https://github.com/geoaigroup/geoai-ecrs2023
공식 코드https://github.com/gesina/bg_randomized_loce
공식 코드On Background Bias of Post-Hoc Concept Embeddings in Computer Vision DNNs
https://github.com/ghadaelkhawaga/ppm_xai_comparison
공식 코드Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?
https://github.com/giacoballoccu/explanation-quality-recsys
공식 코드https://github.com/giacomo97cnr/rule-based-odd
공식 코드https://github.com/giangnguyen2412/dissect_catastrophic_forgetting
공식 코드Explaining How Deep Neural Networks Forget by Deep Visualization
https://github.com/gim4855744/honam
공식 코드https://github.com/gnapoles/ltcn-classifier
공식 코드Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification
https://github.com/goyalpike/RK4_SinDy
공식 코드https://github.com/gregorbaer/class-perturbation-effects
공식 코드Class-Dependent Perturbation Effects in Evaluating Time Series Attributions
https://github.com/groshanlal/nn2rules
공식 코드https://github.com/groshanlal/te2rules
공식 코드https://github.com/gsarridis/faceX
공식 코드FaceX: Understanding Face Attribute Classifiers through Summary Model Explanations
https://github.com/han-yuan-med/template-explanation
공식 코드https://github.com/harry-rogers/psie
공식 코드Deep Learning for Precision Agriculture: Post-Spraying Evaluation and Deposition Estimation
https://github.com/hbaniecki/adversarial-explainable-ai
공식 코드Adversarial attacks and defenses in explainable artificial intelligence: A survey
https://github.com/hcmlab/alterfactuals
공식 코드Relevant Irrelevance: Generating Alterfactual Explanations for Image Classifiers
https://github.com/hillary-ngai/doctor_xavier
공식 코드Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation
https://github.com/hiyouga/llama-efficient-tuning
공식 코드Large Language Models for Cyber Security: A Systematic Literature Review
https://github.com/holistic-ai/HEARTS-Text-Stereotype-Detection
공식 코드HEARTS: A Holistic Framework for Explainable, Sustainable and Robust Text Stereotype Detection
https://github.com/hou-yz/DA_visualization
공식 코드https://github.com/hou-yz/color_distillation
공식 코드https://github.com/hryniewska/ensemblexai
공식 코드NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble Techniques
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.