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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/aimat-lab/gcnn_keras
공식 코드https://github.com/aimat-lab/graph_attention_student
공식 코드https://github.com/aiser-team/cabrnet
공식 코드CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models
https://github.com/aix-group/ppm-survey
공식 코드This looks like what? Challenges and Future Research Directions for Part-Prototype Models
https://github.com/aixplain/NoRefER
공식 코드https://github.com/aklein1995/drise_dmfpp_ddeletion
공식 코드https://github.com/alien9427/xai4sar-pgil
공식 코드https://github.com/alviano/xasp
공식 코드https://github.com/amaa11/Additive-Effects-of-Collinearity
공식 코드Explainable Artificial Intelligence for Dependent Features: Additive Effects of Collinearity
https://github.com/amaa11/mip
공식 코드Characterizing the contribution of dependent features in XAI methods
https://github.com/amamalak/xai_fidelity_assessment_cnn_geo
공식 코드https://github.com/amayags/biox-cpath
공식 코드BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology
https://github.com/aminnayebi/xaicomparison
공식 코드https://github.com/amparore/leaf
공식 코드To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods
https://github.com/analytics-everywhere-lab/heart2mind
공식 코드https://github.com/ananyajana/interpretable_covid19
공식 코드https://github.com/andreastoraas/conceptexplanations_dr_grading
공식 코드Looking into Concept Explanation Methods for Diabetic Retinopathy Classification
https://github.com/anirbansarkar-cs/Ante-hoc_Explainability_Concepts
공식 코드A Framework for Learning Ante-hoc Explainable Models via Concepts
https://github.com/ankurtaly/Attributions
공식 코드https://github.com/annahedstroem/sanity-checks-revisited
공식 코드Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test
https://github.com/annonymoussubmission/explain-cl
공식 코드https://github.com/apple2373/wbcatt
공식 코드WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes
https://github.com/ashutoshaay26/Relational-Boosted-Bandits
공식 코드https://github.com/avrambardas/interpretable-unsupervised-learning
공식 코드https://github.com/awa59kst120df/graph_attention_student
공식 코드https://github.com/basvandervelden/mmvca
공식 코드https://github.com/bbc/cnn-fractional-motion-compensation
공식 코드https://github.com/biomed-AI/MolRep
공식 코드Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction
https://github.com/bips-hb/carfi_paper
공식 코드Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests
https://github.com/boniolp/dCAM
공식 코드dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification
https://github.com/braindatalab/gecobench
공식 코드GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanations
https://github.com/braindatalab/xai-tris
공식 코드https://github.com/brendel-group/causal-understanding-via-visualizations
공식 코드How Well do Feature Visualizations Support Causal Understanding of CNN Activations?
https://github.com/briankeithn/narrative-maps
공식 코드https://github.com/cae67/G2PC_L2PC
공식 코드Algorithm-Agnostic Explainability for Unsupervised Clustering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.