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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://github.com/francesco-sovrano/yai4hu

공식 코드

Generating User-Centred Explanations via Illocutionary Question Answering: From Philosophy to Interfaces

https://github.com/frederikpahde/medical-ai-safety

공식 코드

Ensuring Medical AI Safety: Explainable AI-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data

https://github.com/fudong03/biointelligence

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Do Protein Transformers Have Biological Intelligence?

https://github.com/fy-meng/cohex

공식 코드

CohEx: A Generalized Framework for Cohort Explanation

https://github.com/g8a9/ferret

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ferret: a Framework for Benchmarking Explainers on Transformers

https://github.com/gabrielkasmi/robust_pv_mapping

공식 코드

Space-scale Exploration of the Poor Reliability of Deep Learning Models: the Case of the Remote Sensing of Rooftop Photovoltaic Systems

https://github.com/geemi725/xpertai

공식 코드

Extracting human interpretable structure-property relationships in chemistry using XAI and large language models

https://github.com/geoaigroup/geoai-ecrs2023

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Trainable Noise Model as an XAI evaluation method: application on Sobol for remote sensing image segmentation

https://github.com/ghadaelkhawaga/ppm_xai_comparison

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Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?

https://github.com/giacomo97cnr/rule-based-odd

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Rule-based Out-Of-Distribution Detection

https://github.com/giangnguyen2412/PCNN-src-code-TMRL2024

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PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humans

https://github.com/gregorbaer/class-perturbation-effects

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Class-Dependent Perturbation Effects in Evaluating Time Series Attributions

https://github.com/gsarridis/faceX

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FaceX: Understanding Face Attribute Classifiers through Summary Model Explanations

https://github.com/han-yuan-med/template-explanation

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Clinical Domain Knowledge-Derived Template Improves Post Hoc AI Explanations in Pneumothorax Classification

https://github.com/harry-rogers/psie

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Deep Learning for Precision Agriculture: Post-Spraying Evaluation and Deposition Estimation

https://github.com/havelhuang/eval_xai_robustness

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SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability

https://github.com/hazyresearch/domino

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Domino: Discovering Systematic Errors with Cross-Modal Embeddings

https://github.com/hbaniecki/adversarial-explainable-ai

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Adversarial attacks and defenses in explainable artificial intelligence: A survey

https://github.com/hcmlab/alterfactuals

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Relevant Irrelevance: Generating Alterfactual Explanations for Image Classifiers

https://github.com/hillary-ngai/doctor_xavier

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Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation

https://github.com/hryniewska/ensemblexai

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NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble Techniques

https://github.com/huashen218/convxai

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ConvXAI: Delivering Heterogeneous AI Explanations via Conversations to Support Human-AI Scientific Writing

https://github.com/hungntt/LangXAI

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LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks

https://github.com/hungntt/xai_thyroid

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Towards Trust of Explainable AI in Thyroid Nodule Diagnosis

https://github.com/icanreachyou/EmSHAP

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Energy-based Model for Accurate Shapley Value Estimation in Interpretable Deep Learning Predictive Modeling

https://github.com/intelligence-csd-auth-gr/LionLearn/tree/master/LioNets_V2

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LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information

https://github.com/interpretml/interpret

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GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints

https://github.com/itsaugat/sf_survey

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Even if Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAI

https://github.com/izzayacine/praxp

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Locally-Minimal Probabilistic Explanations

https://github.com/jacobdineen/hiccs2021

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Unified Explanations in Machine Learning Models: A Perturbation Approach

https://github.com/jdieber/WhyModelWhy

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Why model why? Assessing the strengths and limitations of LIME

https://github.com/jgonzalezab/xai-metrics-north-america

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Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches

https://github.com/jinqiang-yu/xcon22

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Eliminating The Impossible, Whatever Remains Must Be True

https://github.com/josesousaribeiro/XAI-Benchmark

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Does Dataset Complexity Matters for Model Explainers?

https://github.com/josesousaribeiro/conexi

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Black Box Model Explanations and the Human Interpretability Expectations -- An Analysis in the Context of Homicide Prediction

https://github.com/josesousaribeiro/exirt

공식 코드

Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust Perspective

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