--- title: "awesome-ai-safety vs do-not-answer" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/giskard-ai-awesome-ai-safety-vs-libr-ai-do-not-answer" tools: ["giskard-ai-awesome-ai-safety", "libr-ai-do-not-answer"] --- # awesome-ai-safety vs do-not-answer *GraphCanon updated Aug 5, 2026* ## 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. [awesome-ai-safety](https://giskard.ai) reports 220 GitHub stars, 39 forks, and 17 open issues, last pushed Apr 14, 2025. [do-not-answer](https://github.com/Libr-AI/do-not-answer) has 339 stars, 29 forks, and 0 open issues, last pushed Jun 7, 2024. Figures are from public GitHub metadata via [awesome-ai-safety's repository](https://github.com/Giskard-AI/awesome-ai-safety) and [do-not-answer's repository](https://github.com/Libr-AI/do-not-answer). | | [awesome-ai-safety](/tools/giskard-ai-awesome-ai-safety.md) | [do-not-answer](/tools/libr-ai-do-not-answer.md) | | --- | --- | --- | | Tagline | A curated list of papers and technical articles on AI Quality & Safety | A Dataset for Evaluating Safeguards in LLMs | | Stars | 220 | 339 | | Forks | 39 | 29 | | Open issues | 17 | 0 | | Language | - | Jupyter Notebook | | Adopt for | 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. | Dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Dual licensing model, datasets under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License and source files under Apache 2.0 license. | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-ai-safety](/tools/giskard-ai-awesome-ai-safety.md) | [do-not-answer](/tools/libr-ai-do-not-answer.md) | | --- | --- | --- | | Days since push | 473d | 788d | | Open issues (now) | 17 | 0 | | Full report | [trust report](/tools/giskard-ai-awesome-ai-safety/trust.md) | [trust report](/tools/libr-ai-do-not-answer/trust.md) | ## Decision facts: awesome-ai-safety - **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. - **Adopt for:** 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. ## Decision facts: do-not-answer - **Adopt for:** Dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses. - **License detail:** Dual licensing model, datasets under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License and source files under Apache 2.0 license. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/giskard-ai-awesome-ai-safety/alternatives) and [do-not-answer alternatives](/tools/libr-ai-do-not-answer/alternatives) ([awesome-ai-safety markdown twin](/tools/giskard-ai-awesome-ai-safety/alternatives.md), [do-not-answer markdown twin](/tools/libr-ai-do-not-answer/alternatives.md)), 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](/compare/giskard-ai-awesome-ai-safety-vs-libr-ai-do-not-answer.md) 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](/tools/giskard-ai-awesome-ai-safety/trust); [do-not-answer trust report](/tools/libr-ai-do-not-answer/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=giskard-ai-awesome-ai-safety`](/api/graphcanon/graph?tool=giskard-ai-awesome-ai-safety) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_