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Explainable Artificial Intelligence (XAI)

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Explainable Artificial Intelligence

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://git.sr.ht/~hyphaebeast/challenging-xai

공식 코드

Challenging common interpretability assumptions in feature attribution explanations

https://github.com/AI4LIFE-GROUP/LLM_Explainer

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In-Context Explainers: Harnessing LLMs for Explaining Black Box Models

https://github.com/AKC23/Harnessing-LLMs-over-transformer-models-for-detecting-Bengali-depressive-text-A-comprehensive-study

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Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive Study

https://github.com/AMR-/JackalCrowdEnv

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XAI-N: Sensor-based Robot Navigation using Expert Policies and Decision Trees

https://github.com/AdriBento/Greybox-

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Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification

https://github.com/AlbertoBarbado/unsupervised-outlier-transparency

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Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVM

https://github.com/Basvanstein/BIAS

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Deep-BIAS: Detecting Structural Bias using Explainable AI

https://github.com/BirkhoffG/CFMark

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Watermarking Counterfactual Explanations

https://github.com/Brown-SciML/pbj

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Improving Explainability of Softmax Classifiers Using a Prototype-Based Joint Embedding Method

https://github.com/CORE-Robotics-Lab/Utility-of-Explainable-AI-NeurIPS2021

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The Utility of Explainable AI in Ad Hoc Human-Machine Teaming

https://github.com/CoDaS-Lab/XAI-BT-SR

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Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching

https://github.com/DBay-ani/Operator_Selection_Learning_Extensions_For_Fanoos

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A Learning-Based Method for Automatic Operator Selection in the Fanoos XAI System

https://github.com/DILiS-lab/drivers-of-predictive-aleatoric-uncertainty

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Identifying Drivers of Predictive Aleatoric Uncertainty

https://github.com/DatrikIntelligence/SoundnessXAI

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On the Soundness of XAI in Prognostics and Health Management (PHM)

https://github.com/Echoslayer/XAI_From_Classical_Models_to_LLMs

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A Comprehensive Guide to Explainable AI: From Classical Models to LLMs

https://github.com/GabrieleLozupone/AXIAL

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AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans

https://github.com/HPAI-BSC/Focus-Metric

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Focus! Rating XAI Methods and Finding Biases

https://github.com/JoakimEdin/explainable-medical-coding

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An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records

https://github.com/MI2DataLab/XAI-tools

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Landscape of R packages for eXplainable Artificial Intelligence

https://github.com/ModelOriented/iBreakDown

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Do Not Trust Additive Explanations

https://github.com/Mythreyi-V/three-phase-fidelity-evaluation

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Developing a Fidelity Evaluation Approach for Interpretable Machine Learning

https://github.com/NVlabs/MCPNet

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MCPNet: An Interpretable Classifier via Multi-Level Concept Prototypes

https://github.com/Purushothaman-natarajan/Under-water-sonar-image-classification

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Underwater SONAR Image Classification and Analysis using LIME-based Explainable Artificial Intelligence

https://github.com/Purushothaman-natarajan/VALE-Explainer

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VALE: A Multimodal Visual and Language Explanation Framework for Image Classifiers using eXplainable AI and Language Models

https://github.com/RaoulHeese/qshaptools

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Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning

https://github.com/RemilYoucef/split-sd4x

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Interpretable Summaries of Black Box Incident Triaging with Subgroup Discovery

https://github.com/TillmannRheude/SegHiResGrad_CAM

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Leveraging CAM Algorithms for Explaining Medical Semantic Segmentation

https://github.com/XiWen0627/MaxEnIRLinCycling

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Discovering Cyclists' Visual Preferences Through Shared Bike Trajectories and Street View Images Using Inverse Reinforcement Learning

https://github.com/a-fsh-r/IBO

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IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in Histopathology

https://github.com/a11to1n3/tsem

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TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series

https://github.com/abacusai/xai-bench

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Synthetic Benchmarks for Scientific Research in Explainable Machine Learning

https://github.com/aeye-lab/etra-2023-bridging-the-gap

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Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models

https://github.com/ahmedmagdiosman/clevr-xai

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Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI

https://github.com/ahmedmagdiosman/simply-clevr-dataset

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Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI

https://github.com/ai-vtrc/exclaim

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ExClaim: Explainable Neural Claim Verification Using Rationalization

https://github.com/ai4life-group/openxai

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OpenXAI: Towards a Transparent Evaluation of Model Explanations

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