A curated list of awesome academic research, books, code of ethics, newsletters, principles, podcast, reports, tools, regulations and standards related to Responsible AI and Human-Centered AI.
- Academic Research
- Books
- Code of Ethics
- Courses
- Data Sets
- Frameworks
- Institutes
- Newsletters
- Principles
- Podcasts
- Reports
- Tools
- Regulations
- Standards
- Citing this repository
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Lacoste, A., Luccioni, A., Schmidt, V., & Dandres, T. (2019). Quantifying the carbon emissions of machine learning. arXiv preprint arXiv:1910.09700. 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., ... & 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)
- Google Research on Responsible AI: https://research.google/pubs/?collection=responsible-ai
Google
- Pipeline-Aware Fairness: http://fairpipe.dssg.io
- 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
- 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
- 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
- CS594 - Causal Inference and Learning
University of Illinois at Chicago
- CS7880 - Rigorous Approaches to Data Privacy
Northeastern University
- CS860 - Algorithms for Private Data Analysis
University of Waterloo
- CIS 4230/5230 - Ethical Algorithm Design
University of Pennsylvania
- A Framework for Ethical Decision Making
Markkula Center for Applied Ethics
- Data Ethics Canvas
Open Data Institute
- RAI Toolkit
US Department of Defense
- Ada Lovelace Institute
United Kingdom
- European Centre for Algorithmic Transparency
- 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
- 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
- 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)
- 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
- ML Commons Safety Benchmark for general purpose AI chat model
- State of AI - from 2018 up to now -
- Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (NIST Special Publication 1270)
NIST
Bias
- CausalAI
Python
Salesforce
- CausalNex
Python
- CausalImpact
R
- Causalinference
Python
- CIMTx: Causal Inference for Multiple Treatments with a Binary Outcome
R
- dagitty
R
- DoWhy
Python
Microsoft
- mediation: Causal Mediation Analysis
R
- MRPC
R
- 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
- PyVacy: Privacy Algorithms for PyTorch
Python
- SEAL
Python
Microsoft
- SmartNoise
Python
OpenDP
- Tensorflow Privacy
Python
Google
- Alibi Detect
Python
- Deepchecks
Python
- drifter
R
- Evidently
Python
- nannyML
Python
- phoenix
Python
- Aequitas' Bias & Fairness Audit Toolkit
Python
- AI360 Toolkit
Python
R
IBM
- EDFfair: Explicitly Deweighted Features
R
- Fairlearn
Python
Microsoft
- Fairmodels
R
University of California
- fairness
R
- FairRankTune
Python
- FairPAN - Fair Predictive Adversarial Network
R
- Themis ML
Python
- What-If Tool
Python
Google
- 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
- Diverse Counterfactual Explanations (DiCE)
Python
Microsoft
- ecco article
Python
- eli5
Python
- eXplainability Toolbox
Python
- ExplainerHub in github
Python
- 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
- 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
- vivid
R
- XAI - An eXplainability toolbox for machine learning
Python
The Institute for Ethical Machine Learning
- xplique
Python
- XAIoGraphs
Python
Telefonica
- Zennit
Python
- imodels
Python
- imodelsX
Python
- interpretML
Python
Microsoft
- Tensorflow Lattice
Python
Google
- Inspect
AISI
Python
- Prometheus
Python
- 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
Python
- Nightshade
University of Chicago
Tool
- Glaze
University of Chicago
Tool
- Fawkes
University of Chicago
Tool
- openXAI
Python
- Modelscan
Python
- NB Defense
Python
- Rebuff Playground
Python
For consumers:
- Code Carbon
Python
- Azure Sustainability Calculator
Microsoft
- Computer Progress
- Dr. Why
R
Warsaw University of Technology
- Responsible AI Widgets
R
Microsoft
- The Data Cards Playbook
Python
Google
- Mercury
Python
BBVA
- Deepchecks
Python
- AudioSeal: Proactive Localized Watermarking
Python
Facebook
- MarkLLM: An Open-Source Toolkit for LLM Watermarking
Python
- 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
- 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.
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.
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 |
Additional standards can be found using the Standards Database.
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."
@misc{arai_repo,
author={Josep Curto},
title={Awesome Responsible Artificial Intelligence},
year={2024},
note={\url{https://github.com/AthenaCore/AwesomeResponsibleAI}}
}
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.