Comparison
awesome-ai-safety vs Failed-ML
Verdict
Pick awesome-ai-safety if 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; pick Failed-ML if failed-ML is compiled around high-profile ML project failures across domains and includes detailed insights from these cases to help understand common pitfalls in implementing machine learning systems.
Markdown twin · awesome-ai-safety alternatives · Failed-ML alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | awesome-ai-safety | Failed-ML |
|---|---|---|
| Maintenance | Dormant (473d since push) As of 3w · github_public_v1 | Dormant (777d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- awesome-ai-safety
- A curated list of papers and technical articles on AI Quality & Safety
- Failed-ML
- Compilation of high-profile real-world examples of failed machine learning projects
Stars
- awesome-ai-safety
- 220
- Failed-ML
- 753
Forks
- awesome-ai-safety
- 39
- Failed-ML
- 51
Open issues
- awesome-ai-safety
- 17
- Failed-ML
- 0
Language
- awesome-ai-safety
- -
- Failed-ML
- -
Adopt for
- awesome-ai-safety
- 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.
- Failed-ML
- Failed-ML is compiled around high-profile ML project failures across domains and includes detailed insights from these cases to help understand common pitfalls in implementing machine learning systems.
Persona
- awesome-ai-safety
- -
- Failed-ML
- -
Runtime
- awesome-ai-safety
- -
- Failed-ML
- -
License
- awesome-ai-safety
- Apache-2.0
- Failed-ML
- MIT
Last pushed
- awesome-ai-safety
- Apr 14, 2025
- Failed-ML
- Jun 14, 2024
Categories
- awesome-ai-safety
- Evaluation & Observability
- Failed-ML
- Evaluation & Observability
Trust and health
Days since push
- awesome-ai-safety
- 473d
- Failed-ML
- 777d
Open issues (now)
- awesome-ai-safety
- 17
- Failed-ML
- 0
Owner type
- awesome-ai-safety
- Organization
- Failed-ML
- User
Full report
- awesome-ai-safety
- Trust report
- Failed-ML
- Trust report
Choose awesome-ai-safety if…
- License: awesome-ai-safety is Apache-2.0, Failed-ML is MIT.
- Pricing: The repository is free to use under the Apache-2.0 license. However, external resources linked might have their own licensing terms or costs..
- Tags unique to awesome-ai-safety: ai safety, ai-alignment, ai-quality, ethical ai.
- When you need an aggregated source to explore topics such as AI alignment, robustness, fairness in ML models.
When NOT to use awesome-ai-safety
- 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.
- This platform may not provide sufficient guidance on hardware-specific testing, where practical constraints diverge from theoretical models.
Choose Failed-ML if…
- License: Failed-ML is MIT, awesome-ai-safety is Apache-2.0.
- Pricing: Open source under MIT license but no additional paid features are mentioned..
- Requirements: Not a software tool that requires installation. Informational repository intended for reading and learning..
- Tags unique to Failed-ML: artificial-intelligence, classification, data-engineering, data-quality.
- When you seek specific historical examples of where machine learning application went wrong, aiding in understanding the potential mistakes and challenges one might face.
When NOT to use Failed-ML
- If you need a prescriptive guide for solving your current project's technical problems rather than learning from industry-wide mistakes.
- When looking for detailed quantitative metrics on failed projects, Failed-ML focuses more on high-level analysis of failure causes rather than specific performance numbers.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Giskard-AI/awesome-ai-safety) · observed Aug 1, 2026
- GitHub forks (Giskard-AI/awesome-ai-safety) · observed Aug 1, 2026
- Last push (Giskard-AI/awesome-ai-safety) · observed Apr 14, 2025
- License file (Apache-2.0) · observed Aug 1, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (kennethleungty/Failed-ML) · observed Jul 31, 2026
- GitHub forks (kennethleungty/Failed-ML) · observed Jul 31, 2026
- Last push (kennethleungty/Failed-ML) · observed Jun 14, 2024
- License file (MIT) · observed Jul 31, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-ai-safety 220 · Failed-ML 753 (synced Aug 1, 2026).
Common questions
- What is the difference between awesome-ai-safety and Failed-ML?
- awesome-ai-safety: A curated list of papers and technical articles on AI Quality & Safety. Failed-ML: Compilation of high-profile real-world examples of failed machine learning projects. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-ai-safety over Failed-ML?
- Choose awesome-ai-safety over Failed-ML when License: awesome-ai-safety is Apache-2.0, Failed-ML is MIT; Pricing: The repository is free to use under the Apache-2.0 license. However, external resources linked might have their own licensing terms or costs.; Tags unique to awesome-ai-safety: ai safety, ai-alignment, ai-quality, ethical ai; When you need an aggregated source to explore topics such as AI alignment, robustness, fairness in ML models.
- When should I choose Failed-ML over awesome-ai-safety?
- Choose Failed-ML over awesome-ai-safety when License: Failed-ML is MIT, awesome-ai-safety is Apache-2.0; Pricing: Open source under MIT license but no additional paid features are mentioned.; Requirements: Not a software tool that requires installation. Informational repository intended for reading and learning.; Tags unique to Failed-ML: artificial-intelligence, classification, data-engineering, data-quality; When you seek specific historical examples of where machine learning application went wrong, aiding in understanding the potential mistakes and challenges one might face.
- When should I avoid awesome-ai-safety?
- 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. This platform may not provide sufficient guidance on hardware-specific testing, where practical constraints diverge from theoretical models.
- When should I avoid Failed-ML?
- If you need a prescriptive guide for solving your current project's technical problems rather than learning from industry-wide mistakes. When looking for detailed quantitative metrics on failed projects, Failed-ML focuses more on high-level analysis of failure causes rather than specific performance numbers.
- Is awesome-ai-safety or Failed-ML more popular on GitHub?
- Failed-ML has more GitHub stars (753 vs 220). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-ai-safety and Failed-ML open source?
- Yes - both are open-source projects on GitHub (awesome-ai-safety: Apache-2.0, Failed-ML: MIT).
- Where can I find alternatives to awesome-ai-safety or Failed-ML?
- GraphCanon lists graph-backed alternatives at awesome-ai-safety alternatives and Failed-ML alternatives (awesome-ai-safety markdown twin, Failed-ML markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, awesome-ai-safety or Failed-ML?
- awesome-ai-safety: Dormant. Failed-ML: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for awesome-ai-safety and Failed-ML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-ai-safety trust report; Failed-ML trust report.