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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/rizavelioglu/ml4prom

공식 코드

Explainable Artificial Intelligence for Improved Modeling of Processes

https://github.com/roan-schellingerhout/jrs_explanations

공식 코드

A Co-design Study for Multi-Stakeholder Job Recommender System Explanations

https://github.com/robincugny/autoxai

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Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI Solutions

https://github.com/ruoyuchen10/vps

공식 코드

Interpreting Object-level Foundation Models via Visual Precision Search

https://github.com/ryo-asashi/midr

공식 코드

midr: Learning from Black-Box Models by Maximum Interpretation Decomposition

https://github.com/salesforce/omnixai

공식 코드

OmniXAI: A Library for Explainable AI

https://github.com/samuelhess/protoshotxai

공식 코드

ProtoShotXAI: Using Prototypical Few-Shot Architecture for Explainable AI

https://github.com/satyamahesh84/SIDU_XAI_CODE

공식 코드

Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method

https://github.com/sbobek/lux

공식 코드

Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanations

https://github.com/scalabunical/bert-xdd

공식 코드

Detecting mental disorder on social media: a ChatGPT-augmented explainable approach

https://github.com/seongjiko/pixleep

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PixleepFlow: A Pixel-Based Lifelog Framework for Predicting Sleep Quality and Stress Level

https://github.com/seulkiyeom/LRP_Pruning_toy_example

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Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning

https://github.com/shakshi12/misleading-covid-vaccination-tweets

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Misleading the Covid-19 vaccination discourse on Twitter: An exploratory study of infodemic around the pandemic

https://github.com/sichao-li/diverse-explanations-physical-sciences

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Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical Sciences

https://github.com/silvia-cor/xaid

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Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New Perspective

https://github.com/sltzgs/xai-regression

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Toward Explainable AI for Regression Models

https://github.com/sltzgs/xai4windpowercurves

공식 코드

XAI for transparent wind turbine power curve models

https://github.com/slymane/vlslice

공식 코드

VLSlice: Interactive Vision-and-Language Slice Discovery

https://github.com/soerenab/AudioMNIST

공식 코드

AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark

https://github.com/sophhan/imlsa_2024

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Interpretable Machine Learning for Survival Analysis

https://github.com/stephaniedroop/av_explanations

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People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AI

https://github.com/sunshenggu/xc_eval_pcdet

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XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection

https://github.com/tavanaei/ExplainableCNN

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Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AI

https://github.com/teodorchiaburu/beexplainable

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Towards ML Methods for Biodiversity: A Novel Wild Bee Dataset and Evaluations of XAI Methods for ML-Assisted Rare Species Annotations

https://github.com/tkn-tub/nn_molecular_communications

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Communicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano Things

https://github.com/tlabarta/helpfulnessofxai

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Study on the Helpfulness of Explainable Artificial Intelligence

https://github.com/tommy-bie/mica

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MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment

https://github.com/tongstevensun/deepfuse

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Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations

https://github.com/trustinai/gdvaecode

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The Gaussian Discriminant Variational Autoencoder (GdVAE): A Self-Explainable Model with Counterfactual Explanations

https://github.com/trustyai-python/experiments

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TrustyAI Explainability Toolkit

https://github.com/tudelft-cda-lab/xai-pipeline

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SoK: Explainable Machine Learning for Computer Security Applications

https://github.com/ukuhl/directionalalienzoo

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For Better or Worse: The Impact of Counterfactual Explanations' Directionality on User Behavior in xAI

https://github.com/ukuhl/introalienzoo

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Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning

https://github.com/ukuhl/plausiblealienzoo

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Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting

https://github.com/understandable-machine-intelligence-lab/quantus

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Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond

https://github.com/unibwtas/collisionpro

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Collision Probability Distribution Estimation via Temporal Difference Learning

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