References

Ananny, Mike, and Kate Crawford. “Seeing without Knowing: Limitations of the Transparency Ideal and Its Application to Algorithmic Accountability.” New Media & Society, December 13, 2016. https://doi.org/10.1177/1461444816676645.

Baack, Stefan and Madeleine Maxwell. “Alternative Data Governance Approaches: Global Landscape Scan and Analysis.” Mozilla Foundation. September 2020. https://foundation.mozilla.org/en/initiatives/data-futures/data-for-empowerment/.

Buolamwini, Joy, and Timnit Gebru. “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification.” In Conference on Fairness, Accountability and Transparency, 77–91. PMLR, 2018. http://proceedings.mlr.press/v81/buolamwini18a.html.

Bullert, B. J. “Progressive Public Relations, Sweatshops, and the Net.” Political Communication 17, no. 4 (October 1, 2000): 403–7. https://doi.org/10.1080/10584600050179022.

Burrell, Jenna. “How the Machine ‘Thinks’: Understanding Opacity in Machine Learning Algorithms.” Big Data & Society 3, no. 1 (January 5, 2016): 205395171562251. https://doi.org/10.1177/2053951715622512.

Cath, Corinne. “Governing Artificial Intelligence: Ethical, Legal and Technical Opportunities and Challenges.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 376, no. 2133 (November 28, 2018): 20180080. https://doi.org/10.1098/rsta.2018.0080.

Costanza-Chock, Sasha. Design Justice: Community-Led Practices to Build the Worlds We Need. Cambridge, MA: MIT Press, 2020.

Delacroix, Sylvie, and Neil Lawrence. “Bottom-Up Data Trusts: Disturbing the ‘One Size Fits All’ Approach to Data Governance.” SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, October 12, 2018. https://doi.org/10.2139/ssrn.3265315.

DiResta, Renee. “Computational Propaganda: If You Make It Trend, You Make It True.” The Yale Review 106, no. 4 (2018): 12–29. https://doi.org/10.1111/yrev.13402.

Dheeru Dua and Casey Graff. UCI Machine Learning Repository. Irvine, California: University of California, School of Information and Computer Science. http://archive.ics.uci.edu/ml. Accessed July 20, 2020.

Elish, M. C. and Tim Hwang. “An AI Pattern Language.” New York: Data & Society, 2017. https://www.datasociety.net/pubs/ia/AI_Pattern_Language.pdf. Accessed May 11, 2020.

European Commission. “White Paper on Artificial Intelligence: A European approach to excellence and trust.” Brussels: European Commission, 2020. Accessed May 11, 2020. https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf.

Fiesler, Casey, Natalie Garrett, and Nathan Beard. “What Do We Teach When We Teach Tech Ethics? A Syllabi Analysis.” In Proceedings of the 51st ACM Technical Symposium on Computer Science Education, 289–295. SIGCSE ’20. Portland, OR, USA: Association for Computing Machinery, 2020. https://doi.org/10.1145/3328778.3366825.

Fjeld, Jessica, Nele Achten, Hannah Hilligoss, Adam Nagy, and Madhulika Srikumar. “Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI.” SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, January 15, 2020. https://doi.org/10.2139/ssrn.3518482.

Gray, Mary L. and Siddharth Suri. Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. Boston: Houghton Mifflin Harcourt, 2019.

Gurses, Seda, and Joris V. J. van Hoboken. 2017. “Privacy After the Agile Turn.” SocArXiv, May 2, 2017. https://doi.org/10.31235/osf.io/9gy73.

Hagiu, Andrei, and Julian Wright. “Multi-Sided Platforms.” SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, March 19, 2015. https://doi.org/10.2139/ssrn.2794582.

Hardjono, Thomas, and Alex Pentland. “Data Cooperatives: Towards a Foundation for Decentralized Personal Data Management.” ArXiv:1905.08819 [Cs], May 21, 2019. http://arxiv.org/abs/1905.08819.

Jensen, Mark A., Vincent Ferretti, Robert L. Grossman, and Louis M. Staudt. “The NCI Genomic Data Commons as an Engine for Precision Medicine.” Blood 130, no. 4 (July 27, 2017): 453–59. https://doi.org/10.1182/blood-2017-03-735654.

Jin, Ginger Zhe. “Artificial Intelligence and Consumer Privacy.” SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, January 1, 2018. https://papers.ssrn.com/abstract=3112040.

Jobin, Anna, Marcello Ienca, and Effy Vayena. “The Global Landscape of AI Ethics Guidelines.” Nature Machine Intelligence 1, no. 9 (September 2019): 389–99. https://doi.org/10.1038/s42256-019-0088-2.

Li, Hanlin, Nicholas Vincent, Janice Tsai, Jofish Kaye, and Brent Hecht. “How Do People Change Their Technology Use in Protest? Understanding.” Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (November 7, 2019): 87:1–87:22. https://doi.org/10.1145/3359189.

Madaio, Michael, Luke Stark, Jennifer Wortman Vaughan, and Hanna Wallach. “Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI.” In CHI Conference on Human Factors in Computing Systems. ACM, 2020. https://www.microsoft.com/en-us/research/publication/co-designing-checklists-to-understand-organizational-challenges-and-opportunities-around-fairness-in-ai/.

Noble, Safiya Umoja. Algorithms of Oppression: How Search Engines Reinforce Racism. New York, NY: New York University Press, 2018.

Ortolano, Leonard, and Anne Shepherd. “Environmental Impact Assessment: Challenges and Opportunities.” Impact Assessment 13, no. 1 (March 1995): 3–30. https://doi.org/10.1080/07349165.1995.9726076.

Sandvig, Christian, Kevin Hamilton, Karrie Karahalios, and Cedric Langbort. “Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms.” Paper presented to “Data and Discrimination: Converting Critical Concerns into Productive Inquiry,” a preconference at the 64th Annual Meeting of the International Communication Association, May 22, 2014; Seattle, WA, USA. http://www-personal.umich.edu/~csandvig/research/Auditing%20Algorithms%20--%20Sandvig%20--%20ICA%202014%20Data%20and%20Discrimination%20Preconference.pdf.

Selbst, Andrew D., and Solon Barocas. “The Intuitive Appeal of Explainable Machines.” SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, March 2, 2018. https://doi.org/10.2139/ssrn.3126971.

Simonyan, Karen, Andrea Vedaldi, and Andrew Zisserman. “Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.” ArXiv:1312.6034 [Cs], April 19, 2014. http://arxiv.org/abs/1312.6034.

Speicher, Till, Muhammad Ali, Giridhari Venkatadri, Filipe Ribeiro, George Arvanitakis, Fabrício Benevenuto, Krishna P Gummadi, Patrick Loiseau, and Alan Mislove. “Potential for Discrimination in Online Targeted Advertising.” In FAT 2018 - Conference on Fairness, Accountability, and Transparency, 81:1–15. New York, United States, 2018. https://hal.archives-ouvertes.fr/hal-01955343.

Ticona, Julia, Alexandra Mateescu, and Alex Rosenblat. “Beyond Disruption: How Tech Shapes Labor Across Domestic Work & Ridehailing.” New York: Data & Society, 2018. Accessed May 11, 2020. https://datasociety.net/library/beyond-disruption/.

Webb, Amy. The Big Nine: How The Tech Titans and Their Thinking Machines Could Warp Humanity. New York: PublicAffairs/Hachette Book Group, 2019.

West, Sarah Myers, Meredith Whittaker, and Kate Crawford., “Discriminating Systems: Gender, Race, and Power in AI.” New York: AI Now Institute, 2019. https://ainowinstitute.org/discriminatingsystems.pdf. Accessed May 11, 2020.

Wykstra, Stephanie. “Developing a More Diverse AI.” Stanford Social Innovation Review, 17, no. 1 (Winter 2019). https://ssir.org/articles/entry/developing_a_more_diverse_ai.