--- title: "awesome-ai-safety vs awesome-automl-papers" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/giskard-ai-awesome-ai-safety-vs-hibayesian-awesome-automl-papers" tools: ["giskard-ai-awesome-ai-safety", "hibayesian-awesome-automl-papers"] --- # awesome-ai-safety vs awesome-automl-papers *GraphCanon updated Aug 4, 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 awesome-automl-papers if awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. [awesome-ai-safety](https://giskard.ai) reports 220 GitHub stars, 39 forks, and 17 open issues, last pushed Apr 14, 2025. [awesome-automl-papers](https://github.com/hibayesian/awesome-automl-papers) has 4.2k stars, 678 forks, and 2 open issues, last pushed Jun 11, 2024. Figures are from public GitHub metadata via [awesome-ai-safety's repository](https://github.com/Giskard-AI/awesome-ai-safety) and [awesome-automl-papers's repository](https://github.com/hibayesian/awesome-automl-papers). | | [awesome-ai-safety](/tools/giskard-ai-awesome-ai-safety.md) | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | | --- | --- | --- | | Tagline | A curated list of papers and technical articles on AI Quality & Safety | A curated list of automated machine learning papers and resources. | | Stars | 220 | 4,155 | | Forks | 39 | 678 | | Open issues | 17 | 2 | | Language | - | - | | 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. | awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-ai-safety](/tools/giskard-ai-awesome-ai-safety.md) | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | | --- | --- | --- | | Days since push | 473d | 784d | | Open issues (now) | 17 | 2 | | Owner type | Organization | User | | Full report | [trust report](/tools/giskard-ai-awesome-ai-safety/trust.md) | [trust report](/tools/hibayesian-awesome-automl-papers/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: awesome-automl-papers - **Adopt for:** awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. ## 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 awesome-automl-papers if… - Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search. - Also covers Model Training. - When you need a curated list of academic materials to research or learn about AutoML technologies ## 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 awesome-automl-papers - If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources - When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers ## Common questions ### What is the difference between awesome-ai-safety and awesome-automl-papers? awesome-ai-safety: A curated list of papers and technical articles on AI Quality & Safety. awesome-automl-papers: A curated list of automated machine learning papers and resources.. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-ai-safety over awesome-automl-papers? Choose awesome-ai-safety over awesome-automl-papers 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 awesome-automl-papers over awesome-ai-safety? Choose awesome-automl-papers over awesome-ai-safety when Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search; Also covers Model Training; When you need a curated list of academic materials to research or learn about AutoML technologies. ### 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 awesome-automl-papers? If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers ### Is awesome-ai-safety or awesome-automl-papers more popular on GitHub? awesome-automl-papers has more GitHub stars (4,155 vs 220). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-ai-safety and awesome-automl-papers open source? Yes - both are open-source projects on GitHub (awesome-ai-safety: Apache-2.0, awesome-automl-papers: Apache-2.0). ### Where can I find alternatives to awesome-ai-safety or awesome-automl-papers? GraphCanon lists graph-backed alternatives at [awesome-ai-safety alternatives](/tools/giskard-ai-awesome-ai-safety/alternatives) and [awesome-automl-papers alternatives](/tools/hibayesian-awesome-automl-papers/alternatives) ([awesome-ai-safety markdown twin](/tools/giskard-ai-awesome-ai-safety/alternatives.md), [awesome-automl-papers markdown twin](/tools/hibayesian-awesome-automl-papers/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-hibayesian-awesome-automl-papers.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 awesome-automl-papers? awesome-ai-safety: Dormant. awesome-automl-papers: 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 awesome-automl-papers? 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); [awesome-automl-papers trust report](/tools/hibayesian-awesome-automl-papers/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/_