Comparison
awesome-ai-safety vs do-not-answer
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 do-not-answer if dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses.
Markdown twin · awesome-ai-safety alternatives · do-not-answer alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | awesome-ai-safety | do-not-answer |
|---|---|---|
| Maintenance | Dormant (473d since push) As of 3w · github_public_v1 | Dormant (788d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 2w · 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
- do-not-answer
- A Dataset for Evaluating Safeguards in LLMs
Stars
- awesome-ai-safety
- 220
- do-not-answer
- 339
Forks
- awesome-ai-safety
- 39
- do-not-answer
- 29
Open issues
- awesome-ai-safety
- 17
- do-not-answer
- 0
Language
- awesome-ai-safety
- -
- do-not-answer
- Jupyter Notebook
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.
- do-not-answer
- Dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses.
Persona
- awesome-ai-safety
- -
- do-not-answer
- -
Runtime
- awesome-ai-safety
- -
- do-not-answer
- -
License
- awesome-ai-safety
- Apache-2.0
- do-not-answer
- Dual licensing model, datasets under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License and source files under Apache 2.0 license.
Last pushed
- awesome-ai-safety
- Apr 14, 2025
- do-not-answer
- Jun 7, 2024
Categories
- awesome-ai-safety
- Evaluation & Observability
- do-not-answer
- Evaluation & Observability
Trust and health
Days since push
- awesome-ai-safety
- 473d
- do-not-answer
- 788d
Open issues (now)
- awesome-ai-safety
- 17
- do-not-answer
- 0
Full report
- awesome-ai-safety
- Trust report
- do-not-answer
- Trust report
Choose awesome-ai-safety if…
- 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, ai safety, ai-alignment, ai-quality.
- 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 do-not-answer if…
- Tags unique to do-not-answer: datasets, llm-evaluation, safeguard testing.
- To assess the reliability of safeguards implemented in your Large Language Model.
- More GitHub stars (339 vs 220) - visibility, not fit.
When NOT to use do-not-answer
- If you require tools for direct implementation or fine-tuning LLMs rather than evaluating them.
- Your project does not involve assessing ethical compliance or safeguard measures within language models.
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 (Libr-AI/do-not-answer) · observed Aug 5, 2026
- GitHub forks (Libr-AI/do-not-answer) · observed Aug 5, 2026
- Last push (Libr-AI/do-not-answer) · observed Jun 7, 2024
- License file (Apache-2.0) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-ai-safety 220 · do-not-answer 339 (synced Aug 1, 2026).
Common questions
- What is the difference between awesome-ai-safety and do-not-answer?
- awesome-ai-safety: A curated list of papers and technical articles on AI Quality & Safety. do-not-answer: A Dataset for Evaluating Safeguards in LLMs. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-ai-safety over do-not-answer?
- Choose awesome-ai-safety over do-not-answer when 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, ai safety, ai-alignment, ai-quality; When you need an aggregated source to explore topics such as AI alignment, robustness, fairness in ML models.
- When should I choose do-not-answer over awesome-ai-safety?
- Choose do-not-answer over awesome-ai-safety when Tags unique to do-not-answer: datasets, llm-evaluation, safeguard testing; To assess the reliability of safeguards implemented in your Large Language Model; More GitHub stars (339 vs 220) - visibility, not fit.
- 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 do-not-answer?
- If you require tools for direct implementation or fine-tuning LLMs rather than evaluating them. Your project does not involve assessing ethical compliance or safeguard measures within language models.
- Is awesome-ai-safety or do-not-answer more popular on GitHub?
- do-not-answer has more GitHub stars (339 vs 220). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-ai-safety and do-not-answer open source?
- Yes - both are open-source projects on GitHub (awesome-ai-safety: Apache-2.0, do-not-answer: Apache-2.0).
- Where can I find alternatives to awesome-ai-safety or do-not-answer?
- GraphCanon lists graph-backed alternatives at awesome-ai-safety alternatives and do-not-answer alternatives (awesome-ai-safety markdown twin, do-not-answer 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 do-not-answer?
- awesome-ai-safety: Dormant. do-not-answer: 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 do-not-answer?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-ai-safety trust report; do-not-answer trust report.