Human Rights Resources
Human Rights module
- Human rights and their principles. Equality and non-discrimination. A human rights-based approach. : Workshop slides by Asako Hatori, Geneva
Digital technology and human rights:
- Human rights in the digital age - Can they make a difference? : Keynote speech by Michelle Bachelet, UN High Commissioner for Human Rights, Japan Society, New York, 17 October 2019
- Universal Declaration of Human Rights
- International Bill of Human Rights , Fact Sheet No. 2 (rev.1) , UN Human Rights Office
- Convention on the Elimination of All Forms of Discrimination against Women (CEDAW)
- Interantional Convention on the Elimination of Racial Discrimination (ICERD)
- Universal Human Rights Instruments UN Human Rights Office. - list of international human rights instruments applicable globally. Includes both legally binding treaties as well as those are not legally binding per se but set up standards, such as declarations, principles and guidelines)
Curated material to learn more about fairness in AI. The list is indicative and non-exhausted.
- Data feminism D’Ignazio, Catherine, and Lauren F. Klein. The MIT Press, 2020.
- Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor Virginia Eubanks New York: St Martin’s Press, 2017.
- Design Justice: Community-Led Practices to Build the Worlds We Need Costanza-Chock, Sasha. The MIT Press, 2020.
- Algorithms of Oppression: How Search Engines Reinforce Racism. Noble, Safiya Umoja. New York University Press, 2018.
- Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy O’Neil, Cathy. New York: Crown, 2016.
Documentation Processes and Tools
- Datasheets for Datasets Gebru, Timnit, et al. ArXiv:1803.09010 [Cs], Mar. 2020. arXiv.org
- Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science Bender, Emily M., and Batya Friedman. ransactions of the Association for Computational Linguistics, vol. 6, Dec. 2018, pp. 587–604. DOI.org (Crossref), doi:10.1162/tacl_a_00041.
- The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards Holland, Sarah, et al. ArXiv:1805.03677 [Cs], May 2018. arXiv.org
- Model Cards for Model Reporting Mitchell, Margaret, et al. Proceedings of the Conference on Fairness, Accountability, and Transparency, Jan. 2019, pp. 220–29. arXiv.org,
- A Methodology for Creating AI FactSheets Richards, John, et al. ArXiv:2006.13796 [Cs], June 2020. arXiv.org
- A Survey on Bias and Fairness in Machine Learning Mehrabi, Ninareh, et al ArXiv:1908.09635 [Cs], Sept. 2019. arXiv.org
- Fairness in Machine Learning: A Survey Caton, Simon, and Christian Haas ArXiv:2010.04053 [Cs, Stat], Oct. 2020. arXiv.org
- Algorithmic Fairness: Choices, Assumptions, and Definitions Mitchell, Shira, et al. Annual Review of Statistics and Its Application, vol. 8, no. 1, Mar. 2021, pp. 141–63. DOI.org (Crossref), doi:10.1146/annurev-statistics-042720-125902
- Fairness Definitions Explained Verma, Sahil, and Julia Rubin Proceedings of the International Workshop on Software Fairness, ACM, 2018, pp. 1–7. DOI.org (Crossref), doi:10.1145/3194770.3194776.
- Implementing Ai Principles: Frameworks, Processes, and Tools Holland, Sarah, et al. SSRN Scholarly Paper, ID 3783124, Social Science Research Network, 10 Feb. 2021
Machine Learning Fairness
- On the (Im)Possibility of Fairness Friedler, Sorelle A., et al. ArXiv:1609.07236 [Cs, Stat], Sept. 2016. arXiv.org
- Machine Learning for Public Policy: Do We Need to Sacrifice Accuracy to Make Models Fair? Rodolfa, Kit T., et al. ArXiv:2012.02972 [Cs], Dec. 2020. arXiv.org
- On the Apparent Conflict Between Individual and Group Fairness Binns, Reuben arXiv:1912.06883 [cs.LG] (2019) .arXiv.org
- Fairness in Machine Learning: Lessons from Political Philosophy Binns, Reuben Proceedings of Machine Learning Research 81:149-159, 2018 Conference on Fairness, Accountability, and Transparency arXiv:1712.03586
- Fairness, Equality, and Power in Algorithmic Decision-Making Kasy, M., & Abebe, R. FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
Relational Ethics and moral philosophy
- Taking the Basic Structure Seriously Young, Iris Marion Perspectives on Politics 4, no. 01 (March 2006)
- Algorithmic injustice: a relational ethics approach Berhane, Abeba Patterns (N Y). 2021 Feb 12; 2(2): 100205
- From Rationality to Relationality: Ubuntu as an Ethical and Human Rights Framework for Artificial Intelligence Governance Mhlambi, Sabelo Carr Center Discussion Paper 2020-009/annurev-statistics-042720-125902
- From Ethics Washing to Ethics Bashing: A View on Tech Ethics from Within Moral Philosophy Bietti, Elettra (August 30, 2021). Available at SSRN: https://ssrn.com/abstract=3914119
- Whose side are ethics codes on? power, responsibility and the social good. Anne L. Washington and Rachel Kuo. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20)
- Legal and human rights issues of AI: Gaps, challenges and vulnerabilities Rodrigues, Rowena Journal of Responsible Technology, Volume 4, 2020, 100005, ISSN 2666-6596,
- When Good Algorithms Go Sexist: Why and How to Advance AI Gender Equity Smith, Genevieve and Rustagi, Ishita 2021
- Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI Wachter, Sandra and Mittelstadt, Brent and Russell, Chris Computer Law & Security Review 41 (2021)
Coding exercises and the jupyter notebook that is used in the workshop.
A Jupyter notebook with code and exercises to apply in practice the concepts learned
The master thesis that supports the workshop
A complementary terminology for technical and social terms
The abstract for the OSAI'21 workshop on “The Culture of Trustworthy AI. Public debate, education, practical learning” for AI4EU.