A lightning talk presentation for Collaborations Workshop 2022 (CW22) https://www.software.ac.uk/cw22
This is a collaborative project that has been initiated with fellow participants Ben Marwick, Brandeis Marshall, Kirstie Whitaker, Sara Stoudt, Thibault Lestang, and Yacine Jerniteat at the workshop Building Responsible Data Science Workflows: Transparency, Reproducibility, and Ethics by Design, PyData Global 2021 conference, 28–30 October 2021. I would also like to thank Tiffany Timbers, Emma Rand, Ben Marwick, Luc Rocher, the Turing Way Project, and Greg Wilson for helpful Twitter discussions and pointers to resources.
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Narayanan, A., 2018, February. Tutorial: 21 fairness definitions and their politics. In Proc. Conf. Fairness Accountability Transp., New York, USA (Vol. 1170, p. 3). https://www.youtube.com/watch?v=jIXIuYdnyyk
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Ostblom, Joel. 2021. Data Science Ethics. BAIT 509 Business Applications of Machine Learning. https://bait509-ubc.github.io/BAIT509/lectures/lecture10a.html
Stoudt S, Vásquez VN, Martinez CC (2021) Principles for data analysis workflows. PLOS Computational Biology 17(3): e1008770. https://doi.org/10.1371/journal.pcbi.1008770
Srinivasan, R. and Chander, A., 2021. Biases in AI systems. Communications of the ACM, 64(8), pp.44-49. https://doi.org/10.1145/3464903
Suresh, H., Guttag, J., Kaiser, D., & Shah, J. (2021). Understanding Potential Sources of Harm throughout the Machine Learning Life Cycle. MIT Case Studies in Social and Ethical Responsibilities of Computing, (Summer 2021). https://doi.org/10.21428/2c646de5.c16a07bb
The Turing Way Community, Becky Arnold, Louise Bowler, Sarah Gibson, Patricia Herterich, Rosie Higman, … Kirstie Whitaker. (2019, March 25). The Turing Way: A Handbook for Reproducible Data Science (Version v0.0.4). Zenodo. http://doi.org/10.5281/zenodo.3233986
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