awesome-ai-safety
A curated list of papers and technical articles on AI Quality & Safety
GraphCanon updated 2w · GitHub synced 2w
Decision brief
awesome-ai-safety is a curated list of papers and technical articles focused on ensuring AI quality and safety across various machine learning domains including CV and NLP.
Good fit when
- When you need an aggregated source to explore topics such as AI alignment, robustness, fairness in ML models.
- If you are looking for specific examples or literature related to metamorphic testing for decision support systems.
Avoid when
- Not suitable if your requirement is a repository with hands-on coding examples rather than research papers and articles.
- Avoid this resource if you are searching for datasets or tools that are not in the form of academic literature but practical utilities.
- Pricing:
- freemium - The repository is free to use under the Apache-2.0 license. However, external resources linked might have their own licensing terms or costs.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (473d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
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Install
git clone https://github.com/Giskard-AI/awesome-ai-safetyHow it fits your stack(1)
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Evidence and technical details
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Overview
Curated collection covering topics like AI alignment, robustness, fairness, testing approaches, model validation techniques in the context of ML systems, including CV, NLP, and other domains.
Capability facts
No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).
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README
Awesome AI Safety
Figuring out how to make your AI safer? How to avoid ethical biases, errors, privacy leaks or robustness issues in your AI models?
This repository contains a curated list of papers & technical articles on AI Quality & Safety that should help 📚
Table of Contents
You can browse papers by Machine Learning task category, and use hashtags like #robustness to explore AI risk types.
- General ML Testing
- Tabular Machine Learning
- Natural Language Processing
- Computer Vision
- Recommendation System
- Time Series
General ML Testing
- Machine learning testing: Survey, landscapes and horizons (Zhang et al., 2020)
#General - Quality Assurance for AI-based Systems: Overview and Challenges (Felderer et al., 2021)
#General - The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction (Breck et al., 2017)
#General - Reliable Machine Learning: Applying SRE Principles to ML in Production [BOOK] (Chen et al., 2022)
#Reliability - Metamorphic testing of decision support systems: A case study (Kuo et al., 2010)
#Robustness - A Survey on Metamorphic Testing (Segura et al., 2016)
#Robustness - Testing and validating machine learning classifiers by metamorphic testing (Xie et al., 2011)
#Robustness - The Disagreement Problem in Explainable Machine Learning: A Practitioner’s Perspective (Krishna et al., 2022)
#Explainability - InterpretML: A Unified Framework for Machine Learning Interpretability (Nori et al., 2019)
#Explainability#General - Fair regression: Quantitative definitions and reduction-based algorithms (Agarwal et al., 2019)
#Fairness - Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making (Aghaei et al., 2019)
#Fairness - Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning (Henderson et al., 2020)
#Environment
AI Incident Databases
- AI Incident Database (Responsible AI Collaborative)
- AI Vulnerability Database (AVID)
Tabular Machine Learning
- Machine Learning Model Drift Detection Via Weak Data Slices (Ackerman et al., 2021)
#DataSlice#Debugging#Drift - Automated Data Slicing for Model Validation: A Big Data - AI Integration Approach (Chung et al., 2020)
#DataSlice - Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models (Krause et al., 2016)
#Explainability
Natural Language Processing
- Beyond Accuracy: Behavioral Testing of NLP Models with CheckList (Ribeiro et al., 2020)
#Robustness - Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularization (Chen et al. 2023)
#Robustness - Pipelines for Social Bias Testing of Large Language Models (Nozza et al., 2022)
#Bias#Ethics - [Why Should I Trust You?": Explaining the P
For agents
This page has a .md twin and JSON over the API.