[1] Ali Alkhatib, Michael S. Bernstein, and Margaret Levi. 2017. Examining crowd work and gig work through the historical lens of piecework. In Proceedings ot the 2017 CHI Conference on Human Factors in Computing Systems. ACM, Denver Colorado USA, 4599–4616. https://doi.org/10.1145/3025453.3025974
[2] Cecilia Aragon, Shion Guha, Marina Kogan, Michael Muller, and Gina Neff. 2022. Human-Centered Data Science: An Introduction. MIT Press, Cambridge, MA.
[3] Cecilia Aragon, Clayton Hutto, Andy Echenique, Brittany Fiore-Gartland, Yun Huang, Jinyoung Kim, Gina Neff, Wanli Xing, and Joseph Bayer. 2016. Developing a Research Agenda for Human-Centered Data Science. In Conference Companion Publication of the 2016 Conference on Computer Supported Cooperative Work and Social Computing. ACM Press, San Francisco, California, USA, 529–535. https://doi.org/10.1145/2818052.2855518
[4] Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the dangers of stochastic parrots: Can language models be too big?. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. ACM, Virtual Event Canada, 610–623. https://doi.org/10.1145/3442188.3445922
[5] Ruha Benjamin. 2019. Race after Technology: Abolitionist Tools for the New Jim Code. Polity, Medford, MA.
[6] Heidi R. Biggs, Jeffrey Bardzell, and Shaowen Bardzell. 2021. Watching myself watching birds: Abjection, ecological thinking, and posthuman design. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–16. https://doi.org/10.1145/3411764.3445329
[7] Abeba Birhane and Fred Cummins. 2019. Algorithmic injustices: Towards a relational ethics. (arXiv:1912.07376 Dec. 2019). http://arxiv.org/abs/1912.07376
[8] Alan Borning and Michael Muller. 2012. Next steps for value sensitive design. In Proceedings of the 2012 SIGCHI Conference on Human Factors in Computing Systems (CHI ’12). Association for Computing Machinery, New York, NY, USA, 1125–1134. https://doi.org/10.1145/2207676.2208560
[9] Meredith Broussard. [n. d.]. Artificial Unintelligence: How computers misunderstand the world. MIT Press, Cambridge, MA.
[10] Kate Crawford. 2021. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.
[11] Catherine D’Ignazio and Lauren F Klein. 2020. MData Feminism. MIT Press, Cambridge, MA.
[12] Melanie Feinberg. 2017. A design perspective on data. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI ’17). Association for Computing Machinery, New York, NY, USA, 2952–2963. https://doi.org/10.1145/3025453.3025837
[13] Melanie Feinberg. 2017. Material vision. In Proceedings of the 2017 ACM Conferenceon Computer Supported Cooperative Work and Social Computing (CSCW ’17). Association for Computing Machinery, New York, NY, USA, 604–617. https://doi.org/10.1145/2998181.2998204
[14] Paul M. Fitts. 1951. Human Engineering for an Effective Air-Navigation and Traffic-control System. Technical Report. National Research Council. Pages: xxii, 84.
[15] Laura Forlano. 2016. Decentering the human in the design of collaborative cities. Design Issues 32, 3 (July 2016), 42–54. https://doi.org/10.1162/DESI_a_00398
[16] Mary L. Gray and Siddharth Suri. 2019. Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. Houghton Mifflin Harcourt, Boston, MA.
[17] Sandra G. Harding (Ed.). 2004. The feminist standpoint theory reader: Intellectual and political controversies. Routledge, New York, NY.
[18] N. Katherine Hayles. 1999. How We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics. University of Chicago Press.
[19] Oxford Internet Institute. n.d. A to Z of AI. Retrieved Oct 14, 2021 from https://atozofai.withgoogle.com/intl/en-US/
[20] Marina Kogan, Aaron Halfaker, Shion Guha, Cecilia Aragon, Michael Muller, and Stuart Geiger. 2020. Mapping out human-centered data science: Methods, approaches, and best practices. In Companion of the 2020 ACM International Conference on Supporting Group Work. ACM, Sanibel Island Florida USA, 151–156. https://doi.org/10.1145/3323994.3369898
[21] Ida B. Wells Just Data Lab. [n. d.]. About. https://www.thejustdatalab.com/about
[22] Sabina Leonelli. 2015. What counts as scientific data? A relational framework. Philosophy of Science 82, 5 (2015), 810–821. https://doi.org/10.1086/684083 Publisher: [The University of Chicago Press, Philosophy of Science Association].
[23] Jason Edward Lewis, Noelani Arista, Archer Pechawis, and Suzanne Kite. 2018. Making kin with the machines. Journal of Design and Science 3.5 (July 2018). https://doi.org/10.21428/bfafd97b
[24] Subcommandante Insurgente Marcos. 1996. Fourth Declaration of the Lacandon Jungle. Technical Report. https://schoolsforchiapas.org/wp-content/uploads/2014/03/Fourth-Declaration-of-the-Lacandona-Jungle-.pdf
[25] Deborah Mayo. 2020. P-Values on trial: Selective reporting of (best practice guides against) selective reporting. Harvard Data Science Review 2, 1 (2020). https://doi.org/10.1162/99608f92.e2473f6a
[26] Michael Muller, Cecilia Aragon, Shion Guha, Marina Kogan, Gina Neff, Cathrine Seidelin, Katie Shilton, and Anissa Tanweer. 2020. Interrogating data science. In Conference Companion Publication of the 2020 Conference on Computer Supported Cooperative Work and Social Computing. ACM, Virtual Event USA, 467–473. https://doi.org/10.1145/3406865.3418584
[27] Michael Muller, Melanie Feinberg, Timothy George, Steven J. Jackson, Bonnie E. John, Mary Beth Kery, and Samir Passi. 2019. Human-centered study of data science work practices. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, Glasgow Scotland Uk, 1–8. https://doi.org/10.1145/3290607.3299018
[28] Michael Muller, Ingrid Lange, Dakuo Wang, David Piorkowski, Jason Tsay, Q. Vera Liao, Casey Dugan, and Thomas Erickson. 2019. How data science workers work with data: Discovery, capture, curation, design, creation. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–15. https://doi.org/10.1145/3290605.3300356
[29] Michael Muller, Christine T. Wolf, Josh Andres, Michael Desmond, Narendra Nath Joshi, Zahra Ashktorab, Aabhas Sharma, Kristina Brimijoin, Qian Pan, Evelyn Duesterwald, and Casey Dugan. 2021. Designing ground truth and the social life of labels. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–16. https://doi.org/10.1145/3411764.3445402
[30] Gina Neff. 2012. Venture labor: Work and the burden of risk in innovative industries. MIT press.
[31] Gina Neff and Dawn Nafus. 2016. Self-tracking. MIT Press.
[32] Samir Passi. 2021. Making Data Work: The Human and Organizational Lifeworlds of Data Science. Cornell University.
[33] Cathrine Seidelin. 2020. Towards a co-design perspective on data: Foregrounding data in the design and innovation of data-based services. Ph. D. Dissertation. IT University of Copenhagen. https://scienceopen.com/document?vid=50421150-5ba6-4ae7-ac17-ab0a5b3b78cc
[34] Cathrine Seidelin, Yvonne Dittrich, and Erik Grönvall. 2020. Co-designing data experiments: Domain experts’ exploration and experimentation with self-selected data sources. In Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society (NordiCHI ’20). Association for Computing Machinery, New York, NY, USA, 1–11. https://doi.org/10.1145/3419249.3420152
[35] Andrew D. Selbst, danah boyd, Sorelle Friedler, Suresh Venkatasubramanian, and Janet Vertesi. 2018. Fairness and abstraction in sociotechnical systems. In Proceedings of the 2019 ACM Conference on Fairness, Accountability, and Transparency. https://papers.ssrn.com/abstract=3265913
[36] Chirag Shah, Theresa Anderson, Loni Hagen, and Yin Zhang. 2021. An iSchool approach to data science: Human-centered, socially responsible, and context-driven. Journal of the Association for Information Science and Technology 72, 6 (2021), 793–796. https://doi.org/10.1002/asi.24444 _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/asi.24444.
[37] Ben Shneiderman. 2020. Human-centered artificial intelligence: Three fresh ideas. AIS Transactions on Human-Computer Interaction 12, 3 (2020), 109–124. https://doi.org/10.17705/1thci.00131
[38] Nancy Smith, Shaowen Bardzell, and Jeffrey Bardzell. 2017. Designing for cohabitation: Naturecultures, hybrids, and decentering the human in design. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI’17). Association for Computing Machinery, New York, NY, USA, 1714–1725. https://doi.org/10.1145/3025453.3025948
[39] Royal Society. n.d.. Data Management and Use: Governance in the 21st Century. Technical Report. British Academy and The Royal Society. https://royalsociety. org/topics-policy/projects/data-governance/
[40] Anissa Tanweer, Emily Kalah Gade, PM Krafft, and Sarah K Dreier. 2021. Why the Data Revolution Needs Qualitative Thinking. Harvard Data Science Review 3 (2021).
[41] Steve Torrance. 2011. Machine ethics and the idea of a more-than-human moral world. In Machine ethics. Michael Anderson and Susan Leigh Anderson (Eds.). Cambridge University Press, 115–137. https://doi.org/10.1017/ CBO9780511978036
[42] UC Berkeley Division of Computing Data Science & Society. [n. d.]. Human Contexts and Ethics. https://data.berkeley.edu/hce
[43] Michelle Hoda Wilkerson and Joseph L. Polman. 2020. Situating data science: Exploring how relationships to data shape learning. Journal of the Learning Sciences 29, 1 (2020), 1–10. https://doi.org/10.1080/10508406.2019.1705664