Tools

The < AI & Equality > A Human Rights Toolbox has been designed to equip a global community with the training, resources, and social support to learn from and contribute to a Human Rights-based approach.

Library

The AI & Equality Human Rights Toolbox Library features talks by professors, colleagues and thinkers about how to put the principles of a Human Rights-based Approach into action.

Workshop Materials

→ Jupyter notebooks with code and exercises to practically apply <AI & Equality> concepts used in the Workshop.

→ Terminology for technical and social terms.

Resources

→ Papers

Documentation Processes and Tools

Datasheets for Datasets 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.

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.

Design & Development Methodology

Mapping value sensitive design onto AI for social good principles, Umbrello & Van de Poel, 2021/
Developing a framework for responsible innovation, Jack Stilgoe, Richard Owen, & Phil Macnaghten (2013).
Stakeholder roles in artificial intelligence projects, Gloria Miller, 2022.

Audits

End-User Audits: A System Empowering Communities to Lead Large-Scale Investigations of Harmful Algorithmic Behavior, Lam et al., 2022

Join our community

We are committed to advancing human rights-based approaches in AI & encourage anyone interested in learning more from a global perspective to explore and contribute to our community!

Tools

Library

Workshop Materials

Resources

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Join our community

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