https://github.com/jgalego/awesome-safety-critical-ai
A curated list of references on the role of AI in safety-critical systems ⚠️
https://github.com/jgalego/awesome-safety-critical-ai
List: awesome-safety-critical-ai
ai-privacy ai-safety artificial-intelligence awesome awesome-list awesome-lists compliance explainability genai machine-learning red-team reliability responsible-ai safety-critical trustworthy-ai
Last synced: 2 months ago
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A curated list of references on the role of AI in safety-critical systems ⚠️
- Host: GitHub
- URL: https://github.com/jgalego/awesome-safety-critical-ai
- Owner: JGalego
- License: mit
- Created: 2025-02-17T17:23:06.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-02-25T17:00:01.000Z (2 months ago)
- Last Synced: 2025-02-25T18:19:29.804Z (2 months ago)
- Topics: ai-privacy, ai-safety, artificial-intelligence, awesome, awesome-list, awesome-lists, compliance, explainability, genai, machine-learning, red-team, reliability, responsible-ai, safety-critical, trustworthy-ai
- Homepage:
- Size: 10.1 MB
- Stars: 16
- Watchers: 5
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING
- License: LICENSE
- Citation: CITATION.cff
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README
# Awesome Safety-Critical AI
[](https://awesome.re)     
**Welcome to Awesome Safety Critical AI!**
This repository contains a curated list of references on the role of AI in **safety-critical systems**, systems whose failure can result in loss of life, significant property damage or damage to the environment.
In here, you'll find references on safe and responsible AI, reliable ML, AI testing, V&V in AI, real-world case studies, and much, much more.
You can keep up to date by watching this GitHub repo
* [🌟 Editor's Choice](#top-picks)
* [🏃 TLDR](#tldr)
* [📝 Articles](#articles)
* [✍️ Blogs](#blogs)
* [📚 Books](#books)
* [📜 Certifications](#certifications)
* [🎤 Conferences](#conferences)
* [👩🏫 Courses](#courses)
* [📙 Guidelines](#guidelines)
* [🤝 Initiatives](#initiatives)
* [📋 Reports](#reports)
* [📐 Standards](#standards)
* [🛠️ Tools](#tools)
* [📺 Videos](#videos)
* [📄 Whitepapers](#whitepapers)
* [👷🏼 Working Groups](#working-groups)
* [👾 Miscellaneous](#miscellaneous)
* [🏁 Meta](#meta)
* [About Us](#about-us)
* [Contributions](#contributions)
* [Contributors](#contributors)
* [Citation](#citation)
[🔼 Back to top](#toc)
* 🧰 An awesome set of [tools for production-ready ML](https://github.com/EthicalML/awesome-production-machine-learning)
> **A word of caution** ☝️ Use them wisely and remember that *"a sword is only as good as the man [or woman] who wields it"*
* 😈 A collection of [scary use cases of AI](https://github.com/daviddao/awful-ai), which will hopefully raise awareness to its misuses in society
* 💳 The now-classic [high-interest credit card of technical debt](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43146.pdf) paper by Google
* 🤝 An introduction to [trustworthy AI](https://www.semanticscholar.org/paper/Trustworthy-AI-Part-1-Mariani-Rossi/2e550e23511711dae2689322741f9c113c6c506f) by NVIDIA
* 🚩 Lessons-learned from [red teaming hundreds of generative AI products](https://arxiv.org/abs/2501.07238) by Microsoft
* 🚨 Last but not least, the top 10 [risks for LLM applications and Generative AI](https://genai.owasp.org/) by OWASP
[🔼 Back to top](#toc)
If you're in a hurry or just don't like reading, here's a podcast-style breakdown created with [NotebookLM](https://notebooklm.google/) (courtesy of [Pedro Nunes](https://github.com/pedrosaunu) 🙏)
[](https://soundcloud.com/safety-critical-podcasts/safety-critical-ai-101-podcast)
[🔼 Back to top](#toc)
* (Adedjouma *et al.*, 2024) [Engineering Dependable AI Systems](https://hal.science/hal-03700300v1)
* (Bach *et al.*, 2024) [Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature Review](https://ieeexplore.ieee.org/document/10620168)
* (Belani, Vukovic & Car, 2019) [Requirements Engineering Challenges in Building AI-Based Complex Systems](https://arxiv.org/abs/1908.11791)
* (Beyers *et al.*, 2019) [Quantification of the Impact of Random Hardware Faults on Safety-Critical AI Applications: CNN-Based Traffic Sign Recognition Case Study](https://ieeexplore.ieee.org/document/8990333)
* (Bolchini, Cassano & Miele, 2024) [Resilience of Deep Learning applications: a systematic literature review of analysis and hardening techniques](https://arxiv.org/abs/2309.16733)
* (Bloomfield & Rushby, 2025) [Where AI Assurance Might Go Wrong: Initial lessons from engineering of critical systems](https://arxiv.org/abs/2502.03467)
* (Breck *et al.*, 2016) [What’s your ML test score? A rubric for ML production systems](https://research.google/pubs/whats-your-ml-test-score-a-rubric-for-ml-production-systems/)
* (Bullwinkel *et al.*, 2025) [Lessons From Red Teaming 100 Generative AI Products](https://arxiv.org/abs/2501.07238)
* (Burton & Herd, 2023) [Addressing uncertainty in the safety assurance of machine-learning](https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2023.1132580/full)
* (Cummings, 2021) [Rethinking the Maturity of Artificial Intelligence in Safety-Critical Settings](https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/7394)
* (Dalrymple *et al.*, 2025) [Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems](https://arxiv.org/abs/2405.06624)
* (Dutta *et al.*, 2017) [Output range analysis for deep feedforward neural networks](https://arxiv.org/abs/1709.09130)
* (Farahmand & Neu, 2025) [AI Safety for Physical Infrastructures: A Collaborative and Interdisciplinary Approach](https://onlinelibrary.wiley.com/doi/full/10.1111/ffe.14575)
* (Faria, 2018) [Machine learning safety: An overview](https://scsc.uk/e503prog)
* (Gursel *et al.*, 2025) [The role of AI in detecting and mitigating human errors in safety-critical industries: A review](https://www.sciencedirect.com/science/article/abs/pii/S0951832024007531)
* (Habli, Lawton & Porter, 2020) [Artificial intelligence in health care: accountability and safety](https://pmc.ncbi.nlm.nih.gov/articles/PMC7133468/)
* (Hasani *et al.*, 2022) [Trustworthy Artificial Intelligence in Medical Imaging](https://pmc.ncbi.nlm.nih.gov/articles/PMC8785402/)
* (Houben *et al.*, 2022) [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://link.springer.com/chapter/10.1007/978-3-031-01233-4_1)
* (Jamakatel *et al.*, 2024) [A Goal-Directed Dialogue System for Assistance in Safety-Critical Application](https://www.ijcai.org/proceedings/2024/870)
* (Johnson, 2018) [The Increasing Risks of Risk Assessment: On the Rise of Artificial Intelligence and Non-Determinism in Safety-Critical Systems](https://www.dcs.gla.ac.uk/~johnson/papers/SCSC_18.pdf)
* (Khattak *et al.*, 2024) [AI-supported estimation of safety critical wind shear-induced aircraft go-around events utilizing pilot reports](https://www.cell.com/heliyon/fulltext/S2405-8440(24)04600-0)
* (Kuwajima, Yasuoka & Nakae, 2020) [Engineering problems in machine learning systems](https://link.springer.com/article/10.1007/s10994-020-05872-w)
* (Leofante *et al.*, 2018) [Automated Verification of Neural Networks: Advances, Challenges and Perspectives](https://arxiv.org/abs/1805.09938)
* (Li *et al.*, 2022) [Trustworthy AI: From Principles to Practices](https://arxiv.org/abs/2110.01167)
* (Lubana, 2024) [Understanding and Identifying Challenges in Design of Safety-Critical AI Systems](https://deepblue.lib.umich.edu/handle/2027.42/196092)
* (Luckcuck *et al.*, 2019) [Formal Specification and Verification of Autonomous Robotic Systems: A Survey](https://arxiv.org/abs/1807.00048)
* (Lwakatare *et al.*, 2020) [Large-scale machine learning systems in real-world industrial settings: A review of challenges and solutions](https://www.sciencedirect.com/science/article/abs/pii/S0950584920301373)
* (Macher *et al.*, 2021) [Architectural Patterns for Integrating AI Technology into Safety-Critical System](https://dl.acm.org/doi/fullHtml/10.1145/3489449.3490014)
* (Mariani *et al.*, 2023) [Trustworthy AI - Part I](https://www.semanticscholar.org/paper/Trustworthy-AI-Part-1-Mariani-Rossi/2e550e23511711dae2689322741f9c113c6c506f), [II](https://www.semanticscholar.org/paper/Trustworthy-AI-Part-II-Mariani-Rossi/9f354b3a88e6d6512d22ec152e6c6131a1e44cab) and [III](https://www.semanticscholar.org/paper/Trustworthy-AI-Part-III-Mariani-Rossi/ff446b46c5b9b4c0d18849d479fe5645f6182a36)
* (Meyers, Löfstedt & Elmroth, 2023) [Safety-critical computer vision: an empirical survey of adversarial evasion attacks and defenses on computer vision systems](https://link.springer.com/article/10.1007/s10462-023-10521-4)
* (Papernot *et al.*, 2018) [SoK: Security and Privacy in Machine Learning](https://ieeexplore.ieee.org/document/8406613)
* (Pereira & Thomas, 2024) [Challenges of Machine Learning Applied to Safety-Critical Cyber-Physical Systems](https://www.mdpi.com/2504-4990/2/4/31)
* (Perez-Cerrolaza *et al.*, 2024) [Artificial Intelligence for Safety-Critical Systems in Industrial and Transportation Domains: A Survey](https://dl.acm.org/doi/10.1145/3626314)
* (Picardi *et al.*, 2020) [Assurance Argument Patterns and Processes for Machine Learning in Safety-Related Systems](https://ceur-ws.org/Vol-2560/paper17.pdf)
* (Ramos *et al.*, 2024) [Collaborative Intelligence for Safety-Critical Industries: A Literature Review](https://www.mdpi.com/2078-2489/15/11/728)
* (Sculley *et al.*, 2015) [Hidden Technical Debt in Machine Learning Systems](https://proceedings.neurips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf)
* (Seshia, Sadigh & Sastry, 2020) [Towards Verified Artificial Intelligence](https://arxiv.org/abs/1606.08514)
* (Sinha *et al.*, 2020) [Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems](https://arxiv.org/abs/2008.10581)
* (Sousa, Moutinho & Almeida, 2020) [Expert-in-the-loop Systems Towards Safety-critical Machine Learning Technology in Wildfire Intelligence](https://www.climatechange.ai/papers/neurips2020/90)
* (Tambon *et al.*, 2021) [How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review](https://arxiv.org/abs/2107.12045)
* (Uuk *et al.*, 2025) [Effective Mitigations for Systemic Risks from General-Purpose AI](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5021463)
* (Wang & Chung, 2021) [Artificial intelligence in safety-critical systems: a systematic review](https://www.semanticscholar.org/paper/Artificial-intelligence-in-safety-critical-systems%3A-Wang-Chung/dd56d26b7efd78651f9abf530741da8de7ca1a69)
* (Webster *et al.*, 2019) [A corroborative approach to verification and validation of human-robot teams](https://arxiv.org/pdf/1608.07403)
* (Weiding *et al.*. 2024) [Holistic Safety and Responsibility Evaluations of Advanced AI Models](https://arxiv.org/abs/2404.14068v1)
* (Yu *et al.*, 2024) [A Survey on Failure Analysis and Fault Injection in AI Systems](https://arxiv.org/abs/2407.00125)
* (Zhang & Li, 2020) [Testing and verification of neural-network-based safety-critical control software: A systematic literature review](https://www.sciencedirect.com/science/article/pii/S0950584920300471)
* (Zhang *et al.*, 2020) [Machine Learning Testing: Survey, Landscapes and Horizons](https://ieeexplore.ieee.org/document/9000651)
[🔼 Back to top](#toc)
* (Bits & Atoms, 2017) [Designing Effective Policies for Safety-Critical AI](https://bitsandatoms.co/effective-policies-for-safety-critical-ai/)
* (Bits & Chips, 2024) [Verifying and validating AI in safety-critical systems](https://bits-chips.com/article/verifying-and-validating-ai-in-safety-critical-systems/)
* (Clear Prop, 2023) [Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature Review](https://pamirsevincel.substack.com/p/clear-prop-9-forum-79-paper-spotlight)
* (CleverHans Lab, 2016) [Breaking things is easy](https://cleverhans.io/security/privacy/ml/2016/12/16/breaking-things-is-easy.html)
* (DeepMind, 2018) [Building safe artificial intelligence: specification, robustness, and assurance](https://deepmindsafetyresearch.medium.com/building-safe-artificial-intelligence-52f5f75058f1)
* (EETimes, 2023) [Can We Trust AI in Safety Critical Systems?](https://www.eetimes.com/can-we-trust-ai-in-safety-critical-systems/)
* (Embedded, 2024) [The impact of AI/ML on qualifying safety-critical software](https://www.embedded.com/the-impact-of-ai-ml-on-qualifying-safety-critical-software/)
* (Forbes, 2022) [Part 2: Reflections On AI (Historical Safety Critical Systems)](https://www.forbes.com/sites/rahulrazdan/2022/03/13/reflections-on-a-decade-of-ai-part-2/)
* (Ground Truths, 2025) [When Doctors With AI Are Outperformed by AI Alone](https://www.nytimes.com/2025/02/02/opinion/ai-doctors-medicine.html?unlocked_article_code=1.t04.AeZg.kT0qka6kerAi&smid=url-share)
* (Homeland Security, 2022) [Artificial Intelligence, Critical Systems, and the Control Problem](https://www.hstoday.us/featured/artificial-intelligence-critical-systems-and-the-control-problem/)
* (Lynx) [How is AI being used in Aviation?](https://www.lynx.com/executive-blog/artificial-intelligence-in-avionics)
* (MathWorks, 2023) [The Road to AI Certification: The importance of Verification and Validation in AI](https://blogs.mathworks.com/deep-learning/2023/07/11/the-road-to-ai-certification-the-importance-of-verification-and-validation-in-ai)
* (RC Kennedy Consulting, 2021) [The Surprising Brittleness of AI](https://www.rckennedysc.com/news/the-surprising-brittleness-of-ai)
* (restack, 2025) [Safety In Critical AI Systems](https://www.restack.io/p/ai-application-safety-protocols-answer-safety-in-critical-ai-systems-cat-ai)
* (Safety4Sea, 2024) [The risks and benefits of AI translations in safety-critical industries](https://safety4sea.com/the-risks-and-benefits-of-ai-translations-in-safety-critical-industries/)
* (think AI, 2024) [Artificial Intelligence in Safety-Critical Systems](https://medium.com/think-ai/ai-in-safety-critical-systems-6b778f26c965)
[🔼 Back to top](#toc)
* (Chen *et al.*, 2022) [Reliable Machine Learning: Applying SRE Principles to ML in Production](https://www.amazon.com/Reliable-Machine-Learning-Principles-Production/dp/1098106229)
* (Huang, Jin & Ruan, 2023) [Machine Learning Safety](https://link.springer.com/book/10.1007/978-981-19-6814-3)
* (Huyen, 2022) [Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications](https://www.amazon.com/Designing-Machine-Learning-Systems-Production-Ready/dp/1098107969?&_encoding=UTF8&tag=chiphuyen-20&linkCode=ur2&linkId=0a1dbab0e76f5996e29e1a97d45f14a5&camp=1789&creative=9325)
* (Joseph *et al.*, 2019) [Adversarial Machine Learning](https://www.amazon.com/Adversarial-Machine-Learning-Anthony-Joseph/dp/1107043468)
* (Pelillo & Scantamburlo, 2021) [Machines We Trust: Perspectives on Dependable AI](https://www.amazon.com/Machines-We-Trust-Perspectives-Dependable-ebook/dp/B08P46HDYG)
* (Tran, 2024) [Artificial Intelligence for Safety and Reliability Engineering: Methods, Applications, and Challenges](https://link.springer.com/book/10.1007/978-3-031-71495-5)
* (Varshney, 2021) [Trust in Machine Learning](https://www.manning.com/books/trust-in-machine-learning-cx)
[🔼 Back to top](#toc)
* (ISTQB) [Certified Tester AI Testing (CT-AI)](https://www.istqb.org/certifications/certified-tester-ai-testing-ct-ai/)
* (USAII) [Certified AI Scientist (CAIS)](https://www.usaii.org/artificial-intelligence-certifications/certified-artificial-intelligence-scientist)
[🔼 Back to top](#toc)
* (EDCC2025) [20th European Dependable Computing Conference](https://edcc2025.campus.ciencias.ulisboa.pt/index.html)
* (ELLIS) [Robust ML Workshop 2024](https://sites.google.com/view/robustml2024/home)
* (HAI) [Workshop on Sociotechnical AI Safety](https://hai.stanford.edu/november-17-agenda-workshop-sociotechnical-ai-safety)
* (IJCAI-24) [AI for Critical Infrastructure](https://sites.google.com/view/aiforci-ijcai24/home)
* (KDD2023) [Trustworthy machine learning](https://mltrust.github.io/)
* (MITRE) [FAA Artificial Intelligence Safety Assurance: Roadmap and Technical Exchange Meetings](https://na.eventscloud.com/ereg/inactive.php?eventid=768017)
- [AI/ML Components in Safety-Critical Aviation Systems: Selected Concepts and Underlying Principles](https://ntrs.nasa.gov/citations/20240009355)
- [Developing Standards for AI/ML Systems in Civil Aviation: Challenges and Barriers](https://ntrs.nasa.gov/citations/20240000822)
* (NFM-AI-Safety-20) [NFM Workshop on AI Safety](https://sites.google.com/stanford.edu/nfm-ai-safety-20/)
* (MLOps Community) [AI in Production 2024](https://home.mlops.community/public/collections/ai-in-production-2024-02-18)
* (MLOps Community) [LLMs in Production 2023](https://home.mlops.community/public/collections/llms-in-production-conference-part-iii-2023)
* (Robust Intelligence) [ML:Integrity 2022](https://www.mlintegrityconference.com/)
* (SSS'24) [32nd annual Safety-Critical Systems Symposium](https://scsc.uk/e1007)
[🔼 Back to top](#toc)
* [AI for Good Specialization](https://www.deeplearning.ai/courses/ai-for-good/) @ DeepLearning.AI
* [AI Red Teaming](https://learn.microsoft.com/en-us/security/ai-red-team/) @ Microsoft
* [Dependable AI Systems](https://courses.grainger.illinois.edu/ece598rki/fa2023/) @ University of Illinois Urbana-Champaign
* [Limits to Prediction](https://msalganik.github.io/soc555-cos598J_s2024/) @ Princeton University
* [Machine Learning for Healthcare](https://mlhcmit.github.io/) @ MIT
* [Machine Learning in Production](https://mlip-cmu.github.io/) @ Carnegie-Mellon University
* [Machine Learning Security](https://secure-ai.systems/courses/MLSec/W22/index.html) @ Oregon State University
* [Responsible AI](https://github.com/aws-samples/aws-machine-learning-university-responsible-ai) @ Amazon MLU
* [Robustness in Machine Learning](https://jerryzli.github.io/robust-ml-fall19.html) @ University of Washington
* [Security and Privacy of Machine Learning](https://secml.github.io/) @ University of Virginia
* [Trustworthy Artificial Intelligence](https://trustworthy-ml-course.github.io/) @ University of Michigan, Dearborn
* [Trustworthy Machine Learning](https://secure-ai.systems/courses/MLSec/W22/index.html) @ Oregon State University
* [Trustworthy Machine Learning](https://scalabletrustworthyai.github.io) @ University of Tübingen
[🔼 Back to top](#toc)
* (APT Research) [Artificial Intelligence/Machine Learning System Safety](https://www.apt-research.com/capabilities/artificial-intelligence-machine-learning-system-safety/)
* (CAIDP) [Universal Guidelines for AI](https://www.caidp.org/universal-guidelines-for-ai/)
* (DIU) [Reponsible AI Guidelines](https://www.diu.mil/responsible-ai-guidelines)
* (European Commission) [Ethics guidelines for trustworthy AI](https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai)
* (European Union) [The EU AI Act](https://artificialintelligenceact.eu/)
* (Google) [AI Principles](https://ai.google/responsibility/principles/)
* (Google) [SAIF // Secure AI Framework: A practitioner’s guide to navigating AI security](https://saif.google/)
* (Harvard University) [Initial guidelines for the use of Generative AI tools at Harvard](https://www.huit.harvard.edu/ai/guidelines)
* (Homeland Security) [Roles and Responsibilities Framework for Artificial Intelligence in Critical Infrastructure](https://www.dhs.gov/publication/roles-and-responsibilities-framework-artificial-intelligence-critical-infrastructure)
* (Homeland Security) [Safety and Security Guidelines for Critical Infrastructure Owners and Operators](https://www.dhs.gov/publication/safety-and-security-guidelines-critical-infrastructure-owners-and-operators)
* (Inter-Parliamentary Union) [Guidelines for AI in Parliaments](https://www.ipu.org/ai-guidelines)
* (Microsoft) [Responsible AI: Principles and Approach](https://www.microsoft.com/en-us/ai/principles-and-approach)
* (Ministry of Defense) [JSP 936: Dependable Artificial Intelligence (AI) in defense (part 1: directive)](https://www.gov.uk/government/publications/jsp-936-dependable-artificial-intelligence-ai-in-defence-part-1-directive)
* (NCSC) [Guidelines for secure AI system development](https://www.ncsc.gov.uk/collection/guidelines-secure-ai-system-development)
* (OECD) [AI Principles](https://oecd.ai/en/ai-principles)
* (Stanford) [Responsible AI at Stanford](https://uit.stanford.edu/security/responsibleai)
[🔼 Back to top](#toc)
* (Data, Responsible) [Foundations of responsible data management](https://dataresponsibly.github.io/)
* (DEEL) [Dependable, Certifiable & Explainable Artificial Intelligence for Critical Systems](https://www.deel.ai/)
* (FUTURE-AI) [Best practices for trustworthy AI in medicine](https://future-ai.eu/)
* (IRT Saint Exupéry) [AI for Critical Systems Competence Center](https://www.irt-saintexupery.com/ai-for-critical-systems-competence/)
* (ITU) [AI for Good](https://aiforgood.itu.int/)
* (Partnership on AI) [Safety Critical AI](https://partnershiponai.org/program/safety-critical-ai/)
* (RAILS) [Roadmaps for AI Integration in the Rail Sector](https://rails-project.eu/)
* (SustainML) [Sustainable Machine Learning](https://sustainml.eu/)
* [Responsible AI Institute](https://www.responsible.ai/)
* [Center for Responsible AI](https://centerforresponsible.ai/)
* [WASP WARA Public Safety](https://wasp-sweden.org/industrial-cooperation/research-arenas/wara-ps-public-safety/)
[🔼 Back to top](#toc)
* (Air Street Capital) [State of AI Report 2024](https://www.stateof.ai/)
* (CLTC) [The Flight to Safety-Critical AI: Lessons in AI Safety from the Aviation Industry](https://cltc.berkeley.edu/publication/new-report-the-flight-to-safety-critical-ai-lessons-in-ai-safety-from-the-aviation-industry/)
* (FLI) [AI Safety Index 2024](https://futureoflife.org/document/fli-ai-safety-index-2024/)
* (Google) [Responsible AI Progress Report 2025](https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf)
* (Gov.UK) [International AI Safety Report 2025](https://www.gov.uk/government/publications/international-ai-safety-report-2025)
* (LangChain) [State of AI Agents](https://www.langchain.com/stateofaiagents)
* (McKinsey) [Superagency in the workplace: Empowering people to unlock AI’s full potential](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work)
* (Microsoft) [Responsible AI Transparency Report 2024](https://www.microsoft.com/en-us/corporate-responsibility/responsible-ai-transparency-report)
* (PwC) [US Responsible AI Survey](https://www.pwc.com/us/en/tech-effect/ai-analytics/responsible-ai-survey.html)
[🔼 Back to top](#toc)
* [ANSI/UL 4600](https://users.ece.cmu.edu/~koopman/ul4600/index.html) > Standard for Evaluation of Autonomous Products
* [IEEE 7009-2024](https://standards.ieee.org/ieee/7009/7096/) > IEEE Standard for Fail-Safe Design of Autonomous and Semi-Autonomous Systems
* [ISO/IEC 23053:2022](https://www.iso.org/standard/74438.html) > Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)
* [ISO/IEC 23894:2023](https://www.iso.org/standard/77304.html) > Information technology — Artificial intelligence — Guidance on risk management
* [ISO/IEC 38507:2022](https://www.iso.org/standard/56641.html) > Information technology — Governance of IT — Governance implications of the use of artificial intelligence by organizations
* [ISO/IEC 42001:2023](https://www.iso.org/standard/81230.html) > Information technology — Artificial intelligence — Management system
* [ISO/IEC JTC 1/SC 42](https://www.iso.org/committee/6794475/x/catalogue/) > Artificial intelligence
* [NIST AI 100-1](https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10) > Artificial Intelligence Risk Management Framework
* [SAE G-34](https://standardsworks.sae.org/standards-committees/g-34-artificial-intelligence-aviation) > Artificial Intelligence in Aviation
[🔼 Back to top](#toc)
### Adversarial Attacks
* [`bethgelab/foolbox`](https://github.com/bethgelab/foolbox): fast adversarial attacks to benchmark the robustness of ML models in PyTorch, TensorFlow and JAX
* [`Trusted-AI/adversarial-robustness-toolbox`](https://github.com/Trusted-AI/adversarial-robustness-toolbox): a Python library for ML security - evasion, poisoning, extraction, inference - red and blue teams
### Data Management
* [`cleanlab/cleanlab`](https://github.com/cleanlab/cleanlab): data-centric AI package for data quality and ML with messy, real-world data and labels.
* [`facebook/Ax`](https://github.com/facebook/Ax): an accessible, general-purpose platform for understanding, managing, deploying, and automating adaptive experiments
* [`great-expectations/great_expectations`](https://github.com/great-expectations/great_expectations): always know what to expect from your data
* [`iterative/dvc`](https://github.com/iterative/dvc): a command line tool and VS Code Extension to help you develop reproducible ML projects
* [`pydantic/pydantic`](https://github.com/pydantic/pydantic): data validation using Python type hints
* [`tensorflow/data-validation`](https://github.com/tensorflow/data-validation): a library for exploring and validating ML data
* [`unionai-oss/pandera`](https://github.com/unionai-oss/pandera): data validation for scientists, engineers, and analysts seeking correctness
### Model Evaluation
* [`confident-ai/deepeval`](https://github.com/confident-ai/deepeval): a simple-to-use, open-source LLM evaluation framework, for evaluating and testing LLM systems
* [`RobustBench/robustbench`](https://github.com/RobustBench/robustbench): a standardized adversarial robustness benchmark
* [`trust-ai/SafeBench`](https://github.com/trust-ai/SafeBench): a benchmark for evaluating Autonomous Vehicles in safety-critical scenarios
### Model Fairness & Privacy
* [`fairlearn/fairlearn`](https://github.com/fairlearn/fairlearn): a Python package to assess and improve fairness of ML models
* [`pytorch/opacus`](https://github.com/pytorch/opacus): a library that enables training PyTorch models with differential privacy
* [`tensorflow/privacy`](https://github.com/tensorflow/privacy): a library for training ML models with privacy for training data
### Model Intepretability
* [`pytorch/captum`](https://github.com/pytorch/captum): a model interpretability and understanding library for PyTorch
* [`SeldonIO/alibi`](https://github.com/SeldonIO/alibi): a library aimed at ML model inspection and interpretation
### Model Lifecycle
* [`aimhubio/aim`](https://github.com/aimhubio/aim): an easy-to-use and supercharged open-source experiment tracker
* [`comet-ml/opik`](https://github.com/comet-ml/opik): an open-source platform for evaluating, testing and monitoring LLM applications
* [`evidentlyai/evidently`](https://github.com/evidentlyai/evidently): an open-source ML and LLM observability framework
* [`IDSIA/sacred`](https://github.com/IDSIA/sacred): a tool to help you configure, organize, log and reproduce experiments
* [`mlflow/mlflow`](https://github.com/mlflow/mlflow): an open-source platform for the ML lifecycle
* [`wandb/wandfb`](https://github.com/wandb/wandb): a fully-featured AI developer platform
### Model Security
* [`azure/PyRIT`](https://github.com/Azure/PyRIT): risk identification tool to assess the security and safety issues of generative AI systems
* [`ffhibnese/Model-Inversion-Attack-ToolBox`](https://github.com/ffhibnese/Model-Inversion-Attack-ToolBox): a comprehensive toolbox for model inversion attacks and defenses
* [`protectai/llm-guard`](https://github.com/protectai/llm-guard): a comprehensive tool designed to fortify the security of LLMs
### Model Testing & Validation
* [`deepchecks/deepchecks`](https://github.com/deepchecks/deepchecks): an open-source package for validating ML models and data
* [`explodinggradients/ragas`](https://github.com/explodinggradients/ragas): objective metrics, intelligent test generation, and data-driven insights for LLM apps
* [`pytorchfi/pytorchfi`](https://github.com/pytorchfi/pytorchfi): a runtime fault injection tool for PyTorch 🔥
### Miscellaneous
* [`microsoft/robustlearn`](https://github.com/microsoft/robustlearn): a unified library for research on robust ML
### Bleeding Edge ⚗️
> **Just a quick note** 📌 This section includes some promising, open-source tools we're currently testing and evaluating at Critical Software. We prioritize minimal, reliable, security-first, `prod`-ready tools with support for local deployment. **If you know better ones, feel free to reach out to one of the maintainers or open a pull request.**
* [`agno-agi/agno`](https://github.com/agno-agi/agno): a lightweight library for building multi-modal agents
* [`Arize-ai/phoenix`](https://github.com/Arize-ai/phoenix): an open-source AI observability platform designed for experimentation, evaluation, and troubleshooting
* [`BerriAI/litellm`](https://github.com/BerriAI/litellm): all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, Groq, &c.]
* [`browser-use/browser-use`](https://github.com/browser-use/browser-use): make websites accessible for AI agents
* [`Cinnamon/kotaemon`](https://github.com/Cinnamon/kotaemon): an open-source RAG-based tool for chatting with your documents
* [`ComposioHQ/composio`](https://github.com/ComposioHQ/composio): equip's your AI agents & LLMs with 100+ high-quality integrations via function calling
* [`deepset-ai/haystack`](https://github.com/deepset-ai/haystack): orchestration framework to build customizable, production-ready LLM applications
* [`dottxt-ai/outlines`](https://github.com/dottxt-ai/outlines): make LLMs speak the language of every application
* [`DS4SD/docling`](https://github.com/DS4SD/docling): get your documents ready for gen AI
* [`eth-sri/lmql`](https://github.com/eth-sri/lmql): a programming language for LLMs based on a superset of Python
* [`exo-explore/exo`](https://github.com/exo-explore/exo): run your own AI cluster at home with everyday devices 📱💻 🖥️⌚
* [`FlowiseAI/Flowise`](https://github.com/FlowiseAI/Flowise): drag & drop UI to build your customized LLM flow
* [`groq/groq-python`](https://github.com/groq/groq-python): the official Python library for the Groq API
* [`guidance-ai/guidance`](https://github.com/guidance-ai/guidance):
* [`h2oai/h2o-llmstudio`](https://github.com/h2oai/h2o-llmstudio): a framework and no-code GUI for fine-tuning LLMs
* [`hiyouga/LLaMA-Factory`](https://github.com/hiyouga/LLaMA-Factory): unified efficient fine-tuning of 100+ LLMs and VLMs
* [`instructor-ai/instructor`](https://github.com/instructor-ai/instructor): the most popular Python library for working with structured outputs from LLMs
* [`keephq/keep`](https://github.com/keephq/keep): open-source AIOps and alert management platform
* [`khoj-ai/khoj`](https://github.com/khoj-ai/khoj): a self-hostable AI second brain
* [`ItzCrazyKns/Perplexica`](https://github.com/ItzCrazyKns/Perplexica): an AI-powered search engine and open source alternative to Perplexity AI
* [`langfuse/langfuse`](https://github.com/langfuse/langfuse): an open source LLM engineering platform with support for LLM observability, metrics, evals, prompt management, playground, datasets
* [`langgenius/dify`](https://github.com/langgenius/dify): an open-source LLM app development platform, which combines agentic AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production
* [`latitude-dev/latitude-llm`](https://github.com/latitude-dev/latitude-llm): open-source prompt engineering platform to build, evaluate, and refine your prompts with AI
* [`microsoft/data-formulator`](https://github.com/microsoft/data-formulator): transform data and create rich visualizations iteratively with AI 🪄
* [`microsoft/prompty`](https://github.com/microsoft/prompty): an asset class and format for LLM prompts designed to enhance observability, understandability, and portability for developers
* [`Mintplex-Labs/anything-llm`](https://github.com/Mintplex-Labs/anything-llm): all-in-one Desktop & Docker AI application with built-in RAG, AI agents, No-code agent builder, and more
* [`ollama/ollama`](https://github.com/ollama/ollama): get up and running with Llama 3.3, DeepSeek-R1, Phi-4, Gemma 2, and other large LMs
* [`promptfoo/promptfoo`](https://github.com/promptfoo/promptfoo): a developer-friendly local tool for testing LLM applications
* [`run-llama/llama_index`](https://github.com/run-llama/llama_index): the leading framework for building LLM-powered agents over your data
* [`ScrapeGraphAI/Scrapegraph-ai`](https://github.com/ScrapeGraphAI/Scrapegraph-ai): a web scraping python library that uses LLM and direct graph logic to create scraping pipelines for websites and local documents
* [`stanfordnlp/dspy`](https://github.com/stanfordnlp/dspy): the framework for programming - not prompting - language models
* [`topoteretes/cognee`](https://github.com/topoteretes/cognee): reliable LLM memory for AI applications and AI agents
* [`unitaryai/detoxify`](https://github.com/unitaryai/detoxify): trained models and code to predict toxic comments
* [`unslothai/unsloth`](https://github.com/unslothai/unsloth): finetune Llama 3.3, DeepSeek-R1 and reasoning LLMs 2x faster with 70% less memory! 🦥
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* (ESSS, 2024) [AI Revolution Transforming Safety-Critical Systems EXPLAINED!](https://www.youtube.com/watch?v=jD8vHgpm0Zw) with Raghavendra Bhat
* (IVA, 2023) [AI in Safety-Critical Systems](https://www.youtube.com/watch?v=KOEdRK69t9g)
* (MathWorks, 2024) [Incorporating Machine Learning Models into Safety-Critical Systems](https://www.mathworks.com/videos/incorporating-machine-learning-models-into-safety-critical-systems-1711734247499.html) with Lucas García
* (MLOps Community) [Robustness, Detectability, and Data Privacy in AI](https://home.mlops.community/public/videos/robustness-detectability-and-data-privacy-in-ai) with Vinu Sadasivan and Demetrios Brinkmann
* (Stanford, 2022) [Stanford Seminar - Challenges in AI Safety: A Perspective from an Autonomous Driving Company](https://www.youtube.com/watch?v=N5ts_HdOLMU)
* (Stanford, 2024) [Best of - AI and safety critical systems](https://www.youtube.com/watch?v=t5NN0ilvcIk)
* (valgrAI, 2024) [Integrating machine learning into safety-critical systems](https://www.youtube.com/watch?v=HSxwnuxaCoo) with Thomas Dietterich
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* (Fraunhofer) [Dependable AI: How to use Artificial Intelligence even in critical applications?](https://www.iese.fraunhofer.de/en/services/dependable-ai.html)
* (IET) [The Application of Artificial Intelligence in Functional Safety](https://electrical.theiet.org/guidance-and-codes-of-practice/publications-by-category/artificial-intelligence/the-application-of-artificial-intelligence-in-functional-safety/)
* (Thales) [The Challenges of using AI in Critical Systems](https://www.thalesgroup.com/en/worldwide/group/magazine/challenges-using-ai-critical-systems)
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* (CWE) [Artificial Intelligence WG](https://cwe.mitre.org/community/working_groups.html)
* (EUROCAE) [WG-114 / Artificial Intelligence](https://eurocae.net/news/posts/2019/june/new-working-group-wg-114-artificial-intelligence/)
* (Linux Foundation) [ONNX Safety-Related Profile](https://github.com/ericjenn/working-groups/tree/ericjenn-srpwg-wg1/safety-related-profile)
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* [AI Snake Oil, debunking hype about AI’s capabilities and transformative effects](https://www.aisnakeoil.com/)
* [Awful AI](https://github.com/daviddao/awful-ai), a collection of scary AI use cases
* [CO/AI, actionable resources & strategies for the AI era](https://getcoai.com/)
* [CISA's Roadmap for Artificial Intelligence](https://www.cisa.gov/ai)
* [Google's Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
* [Hacker News on The Best Language for Safety-Critical Software](https://news.ycombinator.com/item?id=3943556)
* [MITRE ATLAS to navigate threats to AI systems through real-world insights](https://atlas.mitre.org/)
* [OWASP's Top 10 LLM Applications & Generative AI](https://genai.owasp.org/)
* [Paul Niquette's Software Does Not Fail essay](http://www.niquette.com/paul/issue/softwr02.htm)
* [RobustML](https://robust-ml.github.io/): community-run hub for learning about robust ML
* [SEBoK Verification and Validation of Systems in Which AI is a Key Element](https://sebokwiki.org/wiki/Verification_and_Validation_of_Systems_in_Which_AI_is_a_Key_Element)
* [StackOverflow discussion on Python coding standards for Safety Critical applications](https://stackoverflow.com/questions/69673807/python-coding-standard-for-safety-critical-applications)
* The gospel of Trustworthy AI according to
* [Deloitte](https://www2.deloitte.com/us/en/pages/deloitte-analytics/solutions/ethics-of-ai-framework.html)
* [IBM](https://research.ibm.com/topics/trustworthy-ai)
* [Microsoft](https://blogs.microsoft.com/blog/2024/09/24/microsoft-trustworthy-ai-unlocking-human-potential-starts-with-trust/)
* [NIST](https://www.nist.gov/trustworthy-and-responsible-ai)
* [NVIDIA](https://www.nvidia.com/en-us/ai-data-science/trustworthy-ai/)
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* [safety-critical-systems](https://github.com/topics/safety-critical-systems) GitHub topic
* [Awesome LLM Apps](https://github.com/Shubhamsaboo/awesome-llm-apps): a collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models
* [Awesome Python Data Science](https://github.com/krzjoa/awesome-python-data-science): (probably) the best curated list of data science software in Python
* [Awesome MLOps](https://github.com/kelvins/awesome-mlops): a curated list of awesome MLOps tools
* [Awesome Production ML](https://github.com/EthicalML/awesome-production-machine-learning): a curated list of awesome open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning
* [Awesome Trustworthy AI](https://github.com/MinghuiChen43/awesome-trustworthy-deep-learning): list covering different topics in emerging research areas including but not limited to out-of-distribution generalization, adversarial examples, backdoor attack, model inversion attack, machine unlearning, &c.
* [Awesome Responsible AI](https://github.com/AthenaCore/AwesomeResponsibleAI): a curated list of awesome academic research, books, code of ethics, courses, data sets, frameworks, institutes, maturity models, newsletters, principles, podcasts, reports, tools, regulations and standards related to Responsible, Trustworthy, and Human-Centered AI
* [Awesome Safety Critical](https://github.com/stanislaw/awesome-safety-critical): a list of resources about programming practices for writing safety-critical software
* [Common Weakness Enumeration](https://cwe.mitre.org): discover AI common weaknesses such as improper validation of generative AI output
* [FDA Draft Guidance on AI](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/artificial-intelligence-enabled-device-software-functions-lifecycle-management-and-marketing): regulatory draft guidance from the US Food & Drug Association, which regulates the development and marketing of Medical Devices in the US (open for comments until April 7th 2025)
## About Us
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[Critical Software](https://criticalsoftware.com/en) is a Portuguese company that specializes in safety- and mission-critical software.
Our mission is to **build a better and safer world** by creating safe and reliable solutions for demanding industries like Space, Energy, Banking, Defense and Medical.
We get to work every day with a variety of high-profile companies, such as Airbus, Alstom, BMW, ESA, NASA, Siemens, and Thales.
If it's true that *"everything fails all the time"*, the stuff we do has to fail *less* often... or **not** at all.
> **Are you ready to begin your Critical adventure?** 🚀 Check out our [open roles](https://careers.criticalsoftware.com/).
## Contributions
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📣 **We're actively looking for maintainers and contributors!**
AI is a rapidly developing field and we are extremely open to contributions, whether it be in the form of [issues](https://github.com/JGalego/awesome-safety-critical-ai/issues), [pull requests](https://github.com/JGalego/awesome-safety-critical-ai/pulls) or [discussions](https://github.com/JGalego/awesome-safety-critical-ai/discussions).
For detailed information on how to contribute, please read our **guidelines**.
## Contributors
[🔼 Back to top](#toc)
[](https://github.com/JGalego/awesome-safety-critical-ai/graphs/contributors)
## Citation
[🔼 Back to top](#toc)
If you found this repository helpful, please consider citing it using the following:
```bibtex
@misc{Galego_Awesome_Safety-Critical_AI,
author = {Galego, João},
title = {{Awesome Safety-Critical AI}},
url = {https://github.com/JGalego/awesome-safety-critical-ai}
}
```