#Explainability
Showing 42 of 42 repositories tagged #explainability, ranked by stars
A game theoretic approach to explain the output of any machine learning model.
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Fit interpretable models. Explain blackbox machine learning.
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Interactive architecture diagrams for codebases
[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
XAI - An eXplainability toolbox for machine learning
[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
Papers about explainability of GNNs
Power Tools for AI Engineers With Deadlines
Shapley Interactions and Shapley Values for Machine Learning
Visualization toolkit for neural networks in PyTorch! Demo -->
This is an open-source version of the representation engineering framework for stopping harmful outputs or hallucinations on the level of activations. 100% free, self-hosted and open-source.
Neural network visualization toolkit for tf.keras
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Visualization tool for Graph Neural Networks
Interpretable Causal Diffusion Language Models
Interpret text data with LLMs (sklearn compatible).
PyTorch Explain: Interpretable Deep Learning in Python.
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
Evaluating ChatGPT’s Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness
Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584
Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
streamlit-shap provides a wrapper to display SHAP plots in Streamlit.
Python framework for interpretable protein prediction
Contextual AI adds explainability to different stages of machine learning pipelines - data, training, and inference - thereby addressing the trust gap between such ML systems and their users. It does not refer to a specific algorithm or ML method — instead, it takes a human-centric view and approach to AI.
Adaptive, interpretable wavelets across domains (NeurIPS 2021)
Repository for our NeurIPS 2022 paper "Concept Embedding Models", our NeurIPS 2023 paper "Learning to Receive Help", and our ICML 2025 paper "Avoiding Leakage Poisoning"
Modular Python Toolbox for Fairness, Accountability and Transparency Forensics
Helping AI practitioners better understand their datasets and models in text classification. From ServiceNow.
When the stakes are high, intelligence is only half the equation - reliability is the other ⚠️
Finding semantically meaningful and accurate prompts.
Interpretable text embeddings by asking LLMs yes/no questions (NeurIPS 2024)
The official repo of TimeLlama, an instruction-finetuned Llama2 series that improve complex temporal reasoning ability.
Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.
Local explanations with uncertainty 💐!
Attribution methods that explain image classification models, implemented in PyTorch, and support batch inputs and GPU.
The official implementation of "RouteExplainer: An Explanation Framework for Vehicle Routing Problem" (PAKDD 2024, oral)
This project proposes a novel methodology to automatically learn financial lexicons that outperform the benchmark Loughran-McDonald lexicon in sentiment analysis tasks
This project automates bank credit risk assessment using AI and machine learning models to predict loan defaults. It streamlines the credit process with predictive analytics, model evaluation, explainability (SHAP), and deployment readiness.