linhkid/AwesomeResponsibleAI

A curated list of awesome academic research, books, code of ethics, data sets, institutes, newsletters, principles, podcasts, reports, tools, regulations and standards related to Responsible AI, Trustworthy AI, and Human-Centered AI.

Awesome Responsible AI

A curated list of awesome academic research, books, code of ethics, courses, data sets, frameworks, newsletters, principles, podcast, reports, tools, regulations and standards related to Responsible AI and Human-Centered AI.

Contents

• Academic Research
• Books
• Code of Ethics
• Courses
• Data Sets
• Frameworks
• Institutes
• Newsletters
• Principles
• Podcasts
• Reports
• Tools
• Regulations
• Standards
• Citing this repository

Academic Research

Evaluation (of model explanations)

• Agarwal, C., Krishna, S., Saxena, E., Pawelczyk, M., Johnson, N., Puri, I., ... & Lakkaraju, H. (2022). Openxai: Towards a transparent evaluation of model explanations. Advances in Neural Information Processing Systems, 35, 15784-15799. Article
• Liesenfeld, A., and Dingemanse, M. (2024). Rethinking Open Source Generative AI: Open-Washing and the EU AI Act. In The 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’24). Rio de Janeiro, Brazil: ACM. Article Benchmark

Bias

• Schwartz, R., Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022). Towards a standard for identifying and managing bias in artificial intelligence (Vol. 3, p. 00). US Department of Commerce, National Institute of Standards and Technology. Article NIST

Challenges

• D'Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., ... & Sculley, D. (2022). Underspecification presents challenges for credibility in modern machine learning. Journal of Machine Learning Research, 23(226), 1-61. Article Google

Drift

• Ackerman, S., Dube, P., Farchi, E., Raz, O., & Zalmanovici, M. (2021, June). Machine learning model drift detection via weak data slices. In 2021 IEEE/ACM Third International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest) (pp. 1-8). IEEE. Article IBM
• Ackerman, S., Raz, O., & Zalmanovici, M. (2020, February). FreaAI: Automated extraction of data slices to test machine learning models. In International Workshop on Engineering Dependable and Secure Machine Learning Systems (pp. 67-83). Cham: Springer International Publishing. Article IBM

Explainability

• Dhurandhar, A., Chen, P. Y., Luss, R., Tu, C. C., Ting, P., Shanmugam, K., & Das, P. (2018). Explanations based on the missing: Towards contrastive explanations with pertinent negatives. Advances in neural information processing systems, 31. Article University of Michigan IBM Research
• Dhurandhar, A., Shanmugam, K., Luss, R., & Olsen, P. A. (2018). Improving simple models with confidence profiles. Advances in Neural Information Processing Systems, 31. Article IBM Research
• Gurumoorthy, K. S., Dhurandhar, A., Cecchi, G., & Aggarwal, C. (2019, November). Efficient data representation by selecting prototypes with importance weights. In 2019 IEEE International Conference on Data Mining (ICDM) (pp. 260-269). IEEE. Article Amazon Development Center IBM Research
• Hind, M., Wei, D., Campbell, M., Codella, N. C., Dhurandhar, A., Mojsilović, A., ... & Varshney, K. R. (2019, January). TED: Teaching AI to explain its decisions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (pp. 123-129)Article IBM Research
• Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30. Article, Github University of Washington
• Luss, R., Chen, P. Y., Dhurandhar, A., Sattigeri, P., Zhang, Y., Shanmugam, K., & Tu, C. C. (2021, August). Leveraging latent features for local explanations. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining (pp. 1139-1149). Article IBM Research University of Michigan
• Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). "Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144). Article, Github University of Washington
• Wei, D., Dash, S., Gao, T., & Gunluk, O. (2019, May). Generalized linear rule models. In International conference on machine learning (pp. 6687-6696). PMLR. Article IBM Research
• Contrastive Explanations Method with Monotonic Attribute Functions (Luss et al., 2019)
• Boolean Decision Rules via Column Generation (Light Edition) (Dash et al., 2018) IBM Research
• Towards Robust Interpretability with Self-Explaining Neural Networks (Alvarez-Melis et al., 2018) MIT

Fairness

• Caton, S., & Haas, C. (2024). Fairness in machine learning: A survey. ACM Computing Surveys, 56(7), 1-38. Article
• Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data, 5(2), 153-163. Article
• Coston, A., Mishler, A., Kennedy, E. H., & Chouldechova, A. (2020, January). Counterfactual risk assessments, evaluation, and fairness. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 582-593). Article
• Jesus, S., Saleiro, P., Jorge, B. M., Ribeiro, R. P., Gama, J., Bizarro, P., & Ghani, R. (2024). Aequitas Flow: Streamlining Fair ML Experimentation. arXiv preprint arXiv:2405.05809. Article
• Saleiro, P., Kuester, B., Hinkson, L., London, J., Stevens, A., Anisfeld, A., ... & Ghani, R. (2018). Aequitas: A bias and fairness audit toolkit. arXiv preprint arXiv:1811.05577. Article
• Vasudevan, S., & Kenthapadi, K. (2020, October). Lift: A scalable framework for measuring fairness in ml applications. In Proceedings of the 29th ACM international conference on information & knowledge management (pp. 2773-2780). Article LinkedIn

Ethical Data Products

• Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Iii, H. D., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86-92. Article Google
• Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., ... & Gebru, T. (2019, January). Model cards for model reporting. In Proceedings of the conference on fairness, accountability, and transparency (pp. 220-229). Article Google
• Pushkarna, M., Zaldivar, A., & Kjartansson, O. (2022, June). Data cards: Purposeful and transparent dataset documentation for responsible ai. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 1776-1826). Article Google
• Rostamzadeh, N., Mincu, D., Roy, S., Smart, A., Wilcox, L., Pushkarna, M., ... & Heller, K. (2022, June). Healthsheet: development of a transparency artifact for health datasets. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 1943-1961). Article Google
• Saint-Jacques, G., Sepehri, A., Li, N., & Perisic, I. (2020). Fairness through Experimentation: Inequality in A/B testing as an approach to responsible design. arXiv preprint arXiv:2002.05819. Article LinkedIn

Sustainability

• Lacoste, A., Luccioni, A., Schmidt, V., & Dandres, T. (2019). Quantifying the carbon emissions of machine learning. arXiv preprint arXiv:1910.09700. Article
• P. Li, J. Yang, M. A. Islam, S. Ren, (2023) Making AI Less “Thirsty”: Uncovering and Addressing the Secret Water Footprint of AI Models, arXiv:2304.03271 Article
• Parcollet, T., & Ravanelli, M. (2021). The energy and carbon footprint of training end-to-end speech recognizers. Article
• Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.M., Rothchild, D., So, D., Texier, M. and Dean, J. (2021). Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350. Article
• Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., ... & Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in neural information processing systems, 28. Article Google
• Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., ... & Young, M. (2014, December). Machine learning: The high interest credit card of technical debt. In SE4ML: software engineering for machine learning (NIPS 2014 Workshop) (Vol. 111, p. 112). Article Google
• Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. arXiv preprint arXiv:1906.02243. Article
• Sustainable AI: AI for sustainability and the sustainability of AI (van Wynsberghe, A. 2021). AI and Ethics, 1-6
• Green Algorithms: Quantifying the carbon emissions of computation (Lannelongue, L. et al. 2020)
• C.-J. Wu, R. Raghavendra, U. Gupta, B. Acun, N. Ardalani, K. Maeng, G. Chang, F. Aga, J. Huang, C. Bai, M. Gschwind, A. Gupta, M. Ott, A. Melnikov, S. Candido, D. Brooks, G. Chauhan, B. Lee, H.-H. Lee, K. Hazelwood, Sustainable AI: Environmental implications, challenges and opportunities in Proceedings of the 5th Conference on Machine Learning and Systems (MLSys) (2022) vol. 4, pp. 795–813. Article

Collections

• Google Research on Responsible AI: https://research.google/pubs/?collection=responsible-ai Google
• Pipeline-Aware Fairness: http://fairpipe.dssg.io

Reproducible/Non-Reproducible Research

• Papers with Code
• Papers without Code

Books

Open Access

• Molnar, C. (2020). Interpretable machine learning. Lulu. com. Interpretable Machine Learning Book Explainability Interpretability Transparency R
• Biecek, P., & Burzykowski, T. (2021). Explanatory model analysis: explore, explain, and examine predictive models. Chapman and Hall/CRC. Book Explainability Interpretability Transparency R
• Biecek, P. (2024). Adversarial Model Analysis. Book Safety Red Teaming
• Barrett, M., Gerke, T. & D’Agostino McGowa, L. (2024). Causal Inference in R Book Causal Inference R

Commercial / Propietary / Closed Access

• Trust in Machine Learning (Varshney, K., 2022) Safety Privacy Drift Fairness Interpretability Explainability
• Interpretable AI (Thampi, A., 2022) Explainability Fairness Interpretability
• AI Fairness (Mahoney, T., Varshney, K.R., Hind, M., 2020 Report Fairness
• Practical Fairness (Nielsen, A., 2021) Fairness
• Hands-On Explainable AI (XAI) with Python (Rothman, D., 2020) Explainability
• AI and the Law (Kilroy, K., 2021) Report Trust Law
• Responsible Machine Learning (Hall, P., Gill, N., Cox, B., 2020) Report Law Compliance Safety Privacy
• Privacy-Preserving Machine Learning
• Human-In-The-Loop Machine Learning: Active Learning and Annotation for Human-Centered AI
• Interpretable Machine Learning With Python: Learn to Build Interpretable High-Performance Models With Hands-On Real-World Examples
• Responsible AI (Hall, P., Chowdhury, R., 2023) Governance Safety Drift

Code of Ethics

• ACS Code of Professional Conduct by Australian ICT (Information and Communication Technology)
• AI Standards Hub
• Association for Computer Machinery's Code of Ethics and Professional Conduct
• IEEE Global Initiative for Ethical Considerations in Artificial Intelligence (AI) and Autonomous Systems (AS)
• ISO/IEC's Standards for Artificial Intelligence

Courses

Causality

• CS594 - Causal Inference and Learning University of Illinois at Chicago

Data/AI Ethics

• Introduction to AI Ethics Kaggle
• Practical Data Ethics Fast.ai

Data Privacy

• CS7880 - Rigorous Approaches to Data Privacy Northeastern University
• CS860 - Algorithms for Private Data Analysis University of Waterloo

Ethical Design

• CIS 4230/5230 - Ethical Algorithm Design University of Pennsylvania

Safety

• Introduction to ML Safety Center for AI Safety

Data Sets

• AI Risk Repository
• Common Corpus
• An ImageNet replacement for self-supervised pretraining without humans
• Huggingface Data Sets
• The Stack

Frameworks

• A Framework for Ethical Decision Making Markkula Center for Applied Ethics
• Data Ethics Canvas Open Data Institute
• Deon Python Drivendata
• Ethics & Algorithms Toolkit
• RAI Toolkit US Department of Defense

Institutes

• Ada Lovelace Institute United Kingdom
• European Centre for Algorithmic Transparency
• Center for Human-Compatible AI UC Berkeley
• Center for Responsible AI
• Montreal AI Ethics Institute
• Munich Center for Technology in Society (IEAI) Germany
• National AI Centre's Responsible AI Network Australia
• Open Data Institute United Kingdom
• Stanford University Human-Centered Artificial Intelligence (HAI) United States of America
• The Institute for Ethical AI & Machine Learning
• University of Oxford Institute for Ethics in AI United Kingdom

Newsletters

• AI Snake Oil
• Import AI
• Marcus on AI
• The AI Ethics Brief
• The Machine Learning Engineer

Principles

• Allianz's Principles for a responsible usage of AI Allianz
• Asilomar AI principles
• European Commission's Guidelines for Trustworthy AI
• Google's AI Principles Google
• IEEE's Ethically Aligned Design IEEE
• Microsoft's AI principles Microsoft
• OECD's AI principles OECD
• Telefonica's AI principles Telefonica
• The Institute for Ethical AI & Machine Learning: The Responsible Machine Learning Principles

Additional:

• FAIR Principles Findability Accessibility Interoperability Reuse

Podcasts

• The Human-Centered AI Podcast
• Responsible AI Podcast
• Trustworthy AI

Reports

(AI) Incidents databases

• AI Incident Database
• AI Vulnerability Database (AVID)
• AIAAIC
• AI Badness: An open catalog of generative AI badness
• George Washington University Law School's AI Litigation Database
• Merging AI Incidents Research with Political Misinformation Research: Introducing the Political Deepfakes Incidents Database
• OECD AI Incidents Monitor
• Verica Open Incident Database (VOID)

Market Analysis

• State of AI - from 2018 up to now -
• The AI Index Report - from 2017 up to now - Stanford Institute for Human-Centered Artificial Intelligence

Other

• Four Principles of Explainable Artificial Intelligence NIST Explainability
• Psychological Foundations of Explainability and Interpretability in Artificial Intelligence NIST Explainability
• Inferring Concept Drift Without Labeled Data, 2021 Drift
• Interpretability, Fast Forward Labs, 2020 Interpretability
• Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (NIST Special Publication 1270) NIST Bias
• Auditing machine learning algorithms Auditing

Tools

Benchmarks

• Geekbench AI
• ML Commons Safety Benchmark for general purpose AI chat model
• MLPerf Training Benchmark Training
• MMMU Apple Python
• LiveBench: A Challenging, Contamination-Free LLM Benchmark Contamination free
• τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains Python

Bias

• balance Python Facebook
• smclafify Python Amazon
• SolasAI Python

Causal Inference

• CausalAI Python Salesforce
• CausalNex Python
• CausalImpact R
• Causalinference Python
• Causal Inference 360 Python
• CIMTx: Causal Inference for Multiple Treatments with a Binary Outcome R
• dagitty R
• DoWhy Python Microsoft
• mediation: Causal Mediation Analysis R
• MRPC R

Drift

• Alibi Detect Python
• Deepchecks Python
• drifter R
• Evidently Python
• nannyML Python
• phoenix Python

Fairness

• Aequitas' Bias & Fairness Audit Toolkit Python
• AI360 Toolkit Python R IBM
• EDFfair: Explicitly Deweighted Features R
• EquiPy Python
• Fairlearn Python Microsoft
• Fairmodels R University of California
• fairness R
• FairRankTune Python
• FairPAN - Fair Predictive Adversarial Network R
• OxonFair Python Oxford Internet Institute
• Themis ML Python
• What-If Tool Python Google

Interpretability/Explicability

• [https://github.com/openai/automated-interpretability](Automated interpretability) Python OpenAI
• AI360 Toolkit Python R IBM
• aorsf: Accelerated Oblique Random Survival Forests R
• breakDown: Model Agnostic Explainers for Individual Predictions R
• captum Python PyTorch
• ceterisParibus: Ceteris Paribus Profiles R
• DALEX: moDel Agnostic Language for Exploration and eXplanation Python R
• DALEXtra: extension for DALEX Python R
• Dianna Python
• Diverse Counterfactual Explanations (DiCE) Python Microsoft
• dtreeviz Python
• ecco article Python
• eli5 Python
• explabox Python National Police Lab AI
• eXplainability Toolbox Python
• ExplainaBoard Python Carnegie Mellon University
• ExplainerHub in github Python
• fastshap R
• fasttreeshap Python LinkedIn
• FAT Forensics Python
• flashlight R
• Human Learn Python
• hstats R
• innvestigate Python Neural Networks
• intepretML Python
• interactions: Comprehensive, User-Friendly Toolkit for Probing Interactions R
• kernelshap: Kernel SHAP R
• lime: Local Interpretable Model-Agnostic Explanations R
• Network Dissection Python Neural Networks MIT
• Shap Python
• Shapash Python
• shapper R
• shapviz R
• Skater Python Oracle
• survex R
• teller Python
• TCAV (Testing with Concept Activation Vectors) Python
• truelens Python Truera
• truelens-eval Python Truera
• pre: Prediction Rule Ensembles R
• Vetiver R Python Posit
• vip R
• vivid R
• XAI - An eXplainability toolbox for machine learning Python The Institute for Ethical Machine Learning
• xplique Python
• XAIoGraphs Python Telefonica
• Zennit Python

Interpretable Models

• imodels Python
• imodelsX Python
• interpretML Python Microsoft R
• PiML Toolbox Python
• Tensorflow Lattice Python Google

LLM Evaluation

• DeepEval Python
• evals Python OpenAI
• FBI: Finding Blindspots in LLM Evaluations with Interpretable Checklists Python
• Giskard Python
• Inspect AISI Python
• Moonshoot AI Verify Foundation Python
• Phoenix Arize AI Python
• Prometheus Python
• Rouge Python
• simple evals Python OpenAI

Performance (& Automated ML)

• auditor R
• automl: Deep Learning with Metaheuristic R
• AutoKeras Python
• Auto-Sklearn Python
• DataPerf Python Google
• deepchecks Python
• EloML R
• Featuretools Python
• LOFO Importance Python
• forester R
• metrica: Prediction performance metrics R
• NNI: Neural Network Intelligence Python Microsoft
• performance R
• TensorFlow Model Analysis Python Google
• TPOT Python
• Unleash Python
• Yellowbrick Python
• WeightWatcher (Examples) Python

(AI/Data) Poisoning

• Copyright Traps for Large Language Models Python
• Nightshade University of Chicago Tool
• Glaze University of Chicago Tool
• Fawkes University of Chicago Tool

Privacy

• BackPACK Python
• DataSynthesizer: Privacy-Preserving Synthetic Datasets Python Drexel University University of Washington
• diffpriv R
• Diffprivlib Python IBM
• Discrete Gaussian for Differential Privacy Python IBM
• Opacus Python Facebook
• Privacy Meter Python National University of Singapore
• PyVacy: Privacy Algorithms for PyTorch Python
• SEAL Python Microsoft
• SmartNoise Python OpenDP
• Tensorflow Privacy Python Google

Reliability Evaluation (of post hoc explanation methods)

• openXAI Python

Robustness

• Adversarial Robustness Toolbox (ART) Python
• BackdoorBench Python
• Foolbox Python
• Guardrails Python
• NeMo Guardrails Python Amazon

Safety

• https://github.com/usnistgov/dioptra Python NIST

Security

• Counterfit Python Microsoft
• Modelscan Python
• NB Defense Python
• Rebuff Playground Python

For consumers:

• Have i been pwned?
• Have I Been Trained?

Sustainability

• Code Carbon Python
• Azure Sustainability Calculator Microsoft
• Computer Progress

(RAI) Toolkit

• Dr. Why R Warsaw University of Technology
• Responsible AI Widgets R Microsoft
• The Data Cards Playbook Python Google
• Mercury Python BBVA
• Deepchecks Python

(AI) Watermaring

• AudioSeal: Proactive Localized Watermarking Python Facebook
• MarkLLM: An Open-Source Toolkit for LLM Watermarking Python

Regulations

• Data Protection and Privacy Legislation Worldwide UNCTAD
• Data Protection Laws of the Word
• Digital Policy Alert
• GDPR Comparison
• SCL Artificial Intelligence Contractual Clauses

Canada

• Algorithmic Impact Assessment tool
• Directive on Automated Decision-Making
• Directive on Privacy Practices
• Directive on Security Management
• Directive on Service and Digital
• Policy on Government Security
• Policy on Service and Digital
• Privacy Act

European Union

• Data Act
• Data Governance Act
• Digital Market Act
• Digital Services Act
• EU AI ACT
• General Data Protection Regulation GDPR - Legal text for the EU GDPR regulation 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC
  • GDPR.EU Guide - A project co-funded by the Horizon 2020 Framework programme of the EU which provides a resource for organisations and individuals researching GDPR, including a library of straightforward and up-to-date information to help organisations achieve GDPR compliance (Legal Text).
• Hiroshima Process International Guiding Principles for Advanced AI system

Singapore

• Singapore’s Approach to AI Governance - Verify

United States

• State consumer privacy laws: California (CCPA and its amendment, CPRA), Virginia (VCDPA), and Colorado (ColoPA).
• Specific and limited privacy data laws: HIPAA, FCRA, FERPA, GLBA, ECPA, COPPA, VPPA and FTC.
• EU-U.S. and Swiss-U.S. Privacy Shield Frameworks - The EU-U.S. and Swiss-U.S. Privacy Shield Frameworks were designed by the U.S. Department of Commerce and the European Commission and Swiss Administration to provide companies on both sides of the Atlantic with a mechanism to comply with data protection requirements when transferring personal data from the European Union and Switzerland to the United States in support of transatlantic commerce.
• Executive Order on Maintaining American Leadership in AI - Official mandate by the President of the US to Privacy Act of 1974 - The privacy act of 1974 which establishes a code of fair information practices that governs the collection, maintenance, use and dissemination of information about individuals that is maintained in systems of records by federal agencies.
• Privacy Protection Act of 1980 - The Privacy Protection Act of 1980 protects journalists from being required to turn over to law enforcement any work product and documentary materials, including sources, before it is disseminated to the public.
• AI Bill of Rights - The Blueprint for an AI Bill of Rights is a guide for a society that protects all people from IA threats based on five principles: Safe and Effective Systems, Algorithmic Discrimination Protections, Data Privacy, Notice and Explanation, and Human Alternatives, Consideration, and Fallback.

Standards

Definition

What are standards?

Standards are voluntary, consensus soluctions. They document an agreement on how a material, product, process, or serice should be specified, performed or delivered. They keep people safe and ensure things work. They create confidence and provide security for investment.

IEEE Standards

Domain	Standard	Status	URL
IEEE Standard for Ethical Considerations in Emulated Empathy in Autonomous and Intelligent Systems	IEEE 7014-2024	Published	https://www.iso.org/standard/74296.html

UNE/ISO Standards

Domain	Standard	Status	URL
Gobierno del dato	UNE 0077:2023	Published	https://tienda.aenor.com/norma-une-especificacion-une-0077-2023-n0071116
Gestión del dato	UNE 0078:2023	Published	https://tienda.aenor.com/norma-une-especificacion-une-0078-2023-n0071117
Calidad del dato	UNE 0079:2023	Published	https://tienda.aenor.com/norma-une-especificacion-une-0079-2023-n0071118
Guía de evaluación del Gobierno, Gestión y Gestión de la Calidad del Dato.	UNE 0080:2023	Published	https://tienda.aenor.com/norma-une-especificacion-une-0080-2023-n0071383
Guía de evaluación de la Calidad de un Conjunto de Datos.	UNE 0081:2023	Published	https://tienda.aenor.com/norma-une-especificacion-une-0081-2023-n0071807

ISO/IEC Standards

Domain	Standard	Status	URL
AI Concepts and Terminology	ISO/IEC 22989:2022 Information technology — Artificial intelligence — Artificial intelligence concepts and terminology	Published	https://www.iso.org/standard/74296.html
AI Risk Management	ISO/IEC 23894:2023 Information technology - Artificial intelligence - Guidance on risk management	Published	https://www.iso.org/standard/77304.html
AI Management System	ISO/IEC DIS 42001 Information technology — Artificial intelligence — Management system	Published	https://www.iso.org/standard/81230.html
Trustworthy AI	ISO/IEC TR 24028:2020 Information technology — Artificial intelligence — Overview of trustworthiness in artificial intelligence	Published	https://www.iso.org/standard/77608.html
Biases in AI	ISO/IEC TR 24027:2021 Information technology — Artificial intelligence (AI) — Bias in AI systems and AI aided decision making	Published	https://www.iso.org/standard/77607.html
AI Performance	ISO/IEC TS 4213:2022 Information technology — Artificial intelligence — Assessment of machine learning classification performance	Published	https://www.iso.org/standard/79799.html
Ethical and societal concerns	ISO/IEC TR 24368:2022 Information technology — Artificial intelligence — Overview of ethical and societal concerns	Published	https://www.iso.org/standard/78507.html
Explainability	ISO/IEC AWI TS 6254 Information technology — Artificial intelligence — Objectives and approaches for explainability of ML models and AI systems	Under Development	https://www.iso.org/standard/82148.html
AI Sustainability	ISO/IEC AWI TR 20226 Information technology — Artificial intelligence — Environmental sustainability aspects of AI systems	Under Development	https://www.iso.org/standard/86177.html
AI Verification and Validation	ISO/IEC AWI TS 17847 Information technology — Artificial intelligence — Verification and validation analysis of AI systems	Under Development	https://www.iso.org/standard/85072.html
AI Controllabitlity	ISO/IEC CD TS 8200 Information technology — Artificial intelligence — Controllability of automated artificial intelligence systems	Published	https://www.iso.org/standard/83012.html
Biases in AI	ISO/IEC CD TS 12791 Information technology — Artificial intelligence — Treatment of unwanted bias in classification and regression machine learning tasks	Under Publication	https://www.iso.org/standard/84110.html
AI Impact Assessment	ISO/IEC AWI 42005 Information technology — Artificial intelligence — AI system impact assessment	Under Development	https://www.iso.org/standard/44545.html
Data Quality for AI/ML	ISO/IEC DIS 5259 Artificial intelligence — Data quality for analytics and machine learning (ML) (1 to 6)	Under Development	https://www.iso.org/standard/81088.html
Data Lifecycle	ISO/IEC FDIS 8183 Information technology — Artificial intelligence — Data life cycle framework	Published	https://www.iso.org/standard/83002.html
Audit and Certification	ISO/IEC CD 42006 Information technology — Artificial intelligence — Requirements for bodies providing audit and certification of artificial intelligence management systems	Under Development	https://www.iso.org/standard/44546.html
Transparency	ISO/IEC AWI 12792 Information technology — Artificial intelligence — Transparency taxonomy of AI systems	Under Development	https://www.iso.org/standard/84111.html
AI Quality	ISO/IEC AWI TR 42106 Information technology — Artificial intelligence — Overview of differentiated benchmarking of AI system quality characteristics	Under Development	https://www.iso.org/standard/86903.html
Synthetic Data	ISO/IEC AWI TR 42103 Information technology — Artificial intelligence — Overview of synthetic data in the context of AI systems	Under Development	https://www.iso.org/standard/86899.html
AI Security	ISO/IEC AWI 27090 Cybersecurity — Artificial Intelligence — Guidance for addressing security threats and failures in artificial intelligence systems	Under Development	https://www.iso.org/standard/56581.html
AI Privacy	ISO/IEC AWI 27091 Cybersecurity and Privacy — Artificial Intelligence — Privacy protection	Under Development	https://www.iso.org/standard/56582.html
AI Governance	ISO/IEC 38507:2022 Information technology — Governance of IT — Governance implications of the use of artificial intelligence by organizations	Published	https://www.iso.org/standard/56641.html
AI Safety	ISO/IEC CD TR 5469 Artificial intelligence — Functional safety and AI systems	Published	https://www.iso.org/standard/81283.html
Beneficial AI Systems	ISO/IEC AWI TR 21221 Information technology – Artificial intelligence – Beneficial AI systems	Under Development	https://www.iso.org/standard/86690.html

NIST Standards

• NIST AI Risk Management Framework
• NIST Assessing Risks and Impacts of AI (ARIA)

Additional standards can be found using the Standards Database.

Citing this repository

Contributors with over 50 edits can be named coauthors in the citation of visible names. Otherwise, all contributors with fewer than 50 edits are included under "et al."

Bibtex

@misc{arai_repo,
  author={Josep Curto},
  title={Awesome Responsible Artificial Intelligence},
  year={2024},
  note={\url{https://github.com/AthenaCore/AwesomeResponsibleAI}}
}

ACM, APA, Chicago, and MLA

ACM (Association for Computing Machinery)

Curto, J., et al. 2024. Awesome Responsible Artificial Intelligence. GitHub. https://github.com/AthenaCore/AwesomeResponsibleAI.

APA (American Psychological Association) 7th Edition

Curto, J., et al. (2024). Awesome Responsible Artificial Intelligence. GitHub. https://github.com/AthenaCore/AwesomeResponsibleAI.

Chicago Manual of Style 17th Edition

Curto, J., et al. "Awesome Responsible Artificial Intelligence." GitHub. Last modified 2024. https://github.com/AthenaCore/AwesomeResponsibleAI.

MLA (Modern Language Association) 9th Edition

Curto, J., et al. "Awesome Responsible Artificial Intelligence". GitHub, 2024, https://github.com/AthenaCore/AwesomeResponsibleAI. Accessed 29 May 2024.