--- title: "awesome-ai-safety vs Failed-ML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/giskard-ai-awesome-ai-safety-vs-kennethleungty-failed-ml" tools: ["giskard-ai-awesome-ai-safety", "kennethleungty-failed-ml"] --- # awesome-ai-safety vs Failed-ML *GraphCanon updated Aug 1, 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 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. [awesome-ai-safety](https://giskard.ai) reports 220 GitHub stars, 39 forks, and 17 open issues, last pushed Apr 14, 2025. [Failed-ML](https://towardsdatascience.com/when-ai-goes-astray-high-profile-machine-learning-mishaps-in-the-real-world-26bd58692195) has 753 stars, 51 forks, and 0 open issues, last pushed Jun 14, 2024. Figures are from public GitHub metadata via [awesome-ai-safety's repository](https://github.com/Giskard-AI/awesome-ai-safety) and [Failed-ML's repository](https://github.com/kennethleungty/Failed-ML). | | [awesome-ai-safety](/tools/giskard-ai-awesome-ai-safety.md) | [Failed-ML](/tools/kennethleungty-failed-ml.md) | | --- | --- | --- | | Tagline | A curated list of papers and technical articles on AI Quality & Safety | Compilation of high-profile real-world examples of failed machine learning projects | | Stars | 220 | 753 | | Forks | 39 | 51 | | Open issues | 17 | 0 | | 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. | 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 | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | 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) | [Failed-ML](/tools/kennethleungty-failed-ml.md) | | --- | --- | --- | | Days since push | 473d | 777d | | Open issues (now) | 17 | 0 | | Owner type | Organization | User | | Full report | [trust report](/tools/giskard-ai-awesome-ai-safety/trust.md) | [trust report](/tools/kennethleungty-failed-ml/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: Failed-ML - **Hosting:** self hosted - **Pricing:** freemium - 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. - **Adopt for:** 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. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/giskard-ai-awesome-ai-safety/alternatives) and [Failed-ML alternatives](/tools/kennethleungty-failed-ml/alternatives) ([awesome-ai-safety markdown twin](/tools/giskard-ai-awesome-ai-safety/alternatives.md), [Failed-ML markdown twin](/tools/kennethleungty-failed-ml/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-kennethleungty-failed-ml.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 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](/tools/giskard-ai-awesome-ai-safety/trust); [Failed-ML trust report](/tools/kennethleungty-failed-ml/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/_