awesome-ai-safety
A curated list of papers and technical articles on AI Quality & Safety
GraphCanon updated Sep 25, 2026 · GitHub synced Sep 25, 2026
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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 (528d since push)
- As of Sep 25, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 25, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/Giskard-AI/awesome-ai-safetyHow it fits your stack(1)
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Similar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
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).
Categories
Tags
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...
For agents
This page has a .md twin and JSON over the API.