Learning about Responsible AI On-The-Job: Learning Pathways, Orientations, and Aspirations
Authors: 
Michael Madaio, Shivani Kapania, Rida Qadri, Ding Wang, Andrew Zaldivar, Remi Denton, 
Lauren WilcoxAuthors Info & Claims
Pages 1544 - 1558
Abstract
Prior work has developed responsible AI (RAI) toolkits and studied how AI practitioners use such resources when practicing RAI. However, AI practitioners may not have the relevant skills or knowledge to effectively use RAI resources—particularly as pre-trained AI models have enabled more people to develop AI-based applications. In this paper, we explore current practices and aspirations for learning about RAI on-the-job, by interviewing 16 AI practitioners and 24 RAI educators across 16 organizations. We identify AI practitioners’ learning pathways for RAI, including information foraging and interpersonal learning; the orientations of RAI learning resources towards computational and procedural approaches to RAI; and aspirations for RAI learning, including desires for more sociotechnical approaches to understand potential harms of AI systems—aspirations that can be in tension with organizational priorities. We contribute empirical evidence of what and how AI practitioners are learning about RAI, and we suggest opportunities for the field to better support sociotechnical approaches to learning about RAI on-the-job.
References
[1]
EU AI Act. 2021. Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on Artificial Intelligence (Artificial Intelligence Act) and amending certain Union legislative acts. EU AI Act. Retrieved December 2023 from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206
[2]
Philip E Agre. 2014. Toward a critical technical practice: Lessons learned in trying to reform AI. In Social science, technical systems, and cooperative work. Psychology Press, 131–157.
[3]
Shazeda Ahmed, Klaudia Jaźwińska, Archana Ahlawat, Amy Winecoff, and Mona Wang. 2024. Field-building and the epistemic culture of AI safety. First Monday (2024).
[4]
IBM Resaerch Trusted AI. 2021. AIF360 API. (2021). https://aif360.mybluemix.net/
[5]
Andrea Aler Tubella, Marçal Mora-Cantallops, and Juan Carlos Nieves. 2024. How to teach responsible AI in Higher Education: challenges and opportunities. Ethics and Information Technology 26, 1 (2024), 3.
[6]
Sanna J Ali, Angèle Christin, Andrew Smart, and Riitta Katila. 2023. Walking the Walk of AI Ethics: Organizational Challenges and the Individualization of Risk among Ethics Entrepreneurs. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. 217–226.
[7]
McKane Andrus, Elena Spitzer, Jeffrey Brown, and Alice Xiang. 2021. What we can’t measure, we can’t understand: challenges to demographic data procurement in the pursuit of fairness. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency. 249–260.
[8]
Netta Avnoon. 2021. Data scientists’ identity work: Omnivorous symbolic boundaries in skills acquisition. Work, Employment and Society 35, 2 (2021), 332–349.
[9]
Agathe Balayn, Mireia Yurrita, Jie Yang, and Ujwal Gadiraju. 2023. “Fairness Toolkits, A Checkbox Culture?” On the Factors that Fragment Developer Practices in Handling Algorithmic Harms. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society. 482–495.
[10]
Kenneth A Bamberger and Deirdre K Mulligan. 2011. New governance, chief privacy officers, and the corporate management of information privacy in the United States: An initial inquiry. Law & Policy 33, 4 (2011), 477–508.
[11]
Kenneth A Bamberger and Deirdre K Mulligan. 2015. Privacy on the ground: driving corporate behavior in the United States and Europe. MIT Press.
[12]
Solon Barocas, Anhong Guo, Ece Kamar, Jacquelyn M. Krones, Meredith Ringel Morris, Jennifer Wortman Vaughan, Duncan Wadsworth, and Hanna M. Wallach. 2021. Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (2021).
[13]
Marguerite Barry, Aphra Kerr, and Oliver Smith. 2020. Ethics on the Ground: From Principles to Practice. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (Barcelona, Spain) (FAT* ’20). Association for Computing Machinery, New York, NY, USA, 688. https://doi.org/10.1145/3351095.3375684
[14]
Benjamin S Baumer, Randi L Garcia, Albert Y Kim, Katherine M Kinnaird, and Miles Q Ott. 2022. Integrating data science ethics into an undergraduate major: A case study. Journal of Statistics and Data Science Education 30, 1 (2022), 15–28.
[15]
Abeba Birhane, William Isaac, Vinodkumar Prabhakaran, Mark Diaz, Madeleine Clare Elish, Iason Gabriel, and Shakir Mohamed. 2022. Power to the people? opportunities and challenges for participatory AI. Equity and Access in Algorithms, Mechanisms, and Optimization (2022), 1–8.
[16]
Abeba Birhane, Pratyusha Kalluri, Dallas Card, William Agnew, Ravit Dotan, and Michelle Bao. 2022. The values encoded in machine learning research. In 2022 ACM Conference on Fairness, Accountability, and Transparency. 173–184.
[17]
Theodore S Boone. 2023. The challenge of defining artificial intelligence in the EU AI Act. Journal of Data Protection & Privacy 6, 2 (2023), 180–195.
[18]
Pierre Bourdieu. 1990. The intellectual field: a world apart. In other words: Essays towards a reflexive sociology (1990), 140–49.
[19]
Pierre Bourdieu. 1990. The logic of practice. Stanford university press.
[20]
Karen L Boyd and Katie Shilton. 2021. Adapting ethical sensitivity as a construct to study technology design teams. Proceedings of the ACM on Human-Computer Interaction 5, GROUP (2021), 1–29.
[21]
Virginia Braun and Victoria Clarke. 2019. Reflecting on reflexive thematic analysis. Qualitative research in sport, exercise and health 11, 4 (2019), 589–597.
[22]
Virginia Braun and Victoria Clarke. 2021. Can I use TA? Should I use TA? Should I not use TA? Comparing reflexive thematic analysis and other pattern-based qualitative analytic approaches. Counselling and psychotherapy research 21, 1 (2021), 37–47.
[23]
Kirsten E Bray, Christina Harrington, Andrea G Parker, N’Deye Diakhate, and Jennifer Roberts. 2022. Radical futures: Supporting community-led design engagements through an afrofuturist speculative design toolkit. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 1–13.
[24]
Ann Cavoukian, Scott Taylor, and Martin E Abrams. 2010. Privacy by Design: essential for organizational accountability and strong business practices. Identity in the Information Society 3 (2010), 405–413.
[25]
Karin Knorr Cetina. 1999. Epistemic cultures: How the sciences make knowledge. Harvard University Press.
[26]
Rimika Chaudhury, Philip J Guo, and Parmit K Chilana. 2022. " There’s no way to keep up!": Diverse Motivations and Challenges Faced by Informal Learners of ML. In 2022 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE, 1–11.
[27]
Paul B de Laat. 2021. Companies committed to responsible AI: From principles towards implementation and regulation?Philosophy & technology 34 (2021), 1135–1193.
[28]
Fernando Delgado, Stephen Yang, Michael Madaio, and Qian Yang. 2023. The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice. In Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization. 1–23.
[29]
Wesley Hanwen Deng, Manish Nagireddy, Michelle Seng Ah Lee, Jatinder Singh, Zhiwei Steven Wu, Kenneth Holstein, and Haiyi Zhu. 2022. Exploring how machine learning practitioners (try to) use fairness toolkits. In 2022 ACM Conference on Fairness, Accountability, and Transparency. 473–484.
[30]
Wesley Hanwen Deng, Nur Yildirim, Monica Chang, Motahhare Eslami, Kenneth Holstein, and Michael Madaio. 2023. Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. 705–716.
[31]
Remi Denton, Mark Díaz, Ian Kivlichan, Vinodkumar Prabhakaran, and Rachel Rosen. 2021. Whose ground truth? accounting for individual and collective identities underlying dataset annotation. arXiv preprint arXiv:2112.04554 (2021).
[32]
Mark Díaz, Ian Kivlichan, Rachel Rosen, Dylan Baker, Razvan Amironesei, Vinodkumar Prabhakaran, and Remi Denton. 2022. CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation. In 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT ’22). Association for Computing Machinery, New York, NY, USA, 2342–2351. https://doi.org/10.1145/3531146.3534647
[33]
Kimberly Do, Rock Yuren Pang, Jiachen Jiang, and Katharina Reinecke. 2023. “That’s important, but...”: How Computer Science Researchers Anticipate Unintended Consequences of Their Research Innovations. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–16.
[34]
Mateusz Dolata, Stefan Feuerriegel, and Gerhard Schwabe. 2022. A sociotechnical view of algorithmic fairness. Information Systems Journal 32, 4 (2022), 754–818.
[35]
Daniel Domínguez Figaredo and Julia Stoyanovich. 2023. Responsible AI literacy: A stakeholder-first approach. Big Data & Society 10, 2 (2023), 20539517231219958.
[36]
Brian Dorn and Mark Guzdial. 2010. Learning on the job: characterizing the programming knowledge and learning strategies of web designers. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 703–712.
[37]
Salma Elsayed-Ali, Sara E Berger, Vagner Figueredo De Santana, and Juana Catalina Becerra Sandoval. 2023. Responsible & Inclusive Cards: An Online Card Tool to Promote Critical Reflection in Technology Industry Work Practices. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems(CHI ’23). ACM. https://doi.org/10.1145/3544548.3580771
[38]
Hayden Field. 2022. How microsoft and google use ai red teams to “stress test” their systems. Emerging Tech Brew (2022).
[39]
Casey Fiesler, Mikhaila Friske, Natalie Garrett, Felix Muzny, Jessie J Smith, and Jason Zietz. 2021. Integrating ethics into introductory programming classes. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education. 1027–1033.
[40]
Casey Fiesler, Natalie Garrett, and Nathan Beard. 2020. 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.
[41]
Casey Fiesler, Shannon Morrison, R Benjamin Shapiro, and Amy S Bruckman. 2017. Growing their own: Legitimate peripheral participation for computational learning in an online fandom community. In Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing. 1375–1386.
[42]
Markkula Center for Applied Ethics. 2024. Ethics in Technology Practice Framework and Toolkit. Retrieved Jan 2024 from https://www.scu.edu/ethics-in-technology-practice/
[43]
Paulo Freire. 1996. Pedagogy of the oppressed (revised). New York: Continuum 356 (1996), 357–358.
[44]
Sorelle Friedler, Ranjit Singh, Borhane Blili-Hamelin, Jacob Metcalf, and Brian J Chen. [n. d.]. AI Red-Teaming Is Not a One-Stop Solution to AI Harms. ([n. d.]). https://datasociety.net/library/ai-red-teaming-is-not-a-one-stop-solution-to-ai-harms-recommendations-for-using-red-teaming-for-ai-accountability/
[45]
Batya Friedman and Helen Nissenbaum. 1996. Bias in computer systems. ACM Transactions on information systems (TOIS) 14, 3 (1996), 330–347.
[46]
G7. 2023. Hiroshima Process International Code of Conduct for Advanced AI Systems. G7 leaders. Retrieved Novemeber 2023 from https://digital-strategy.ec.europa.eu/en/library/hiroshima-process-international-code-conduct-advanced-ai-systems
[47]
Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, 2022. Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned. arXiv preprint arXiv:2209.07858 (2022).
[48]
Natalie Garrett, Nathan Beard, and Casey Fiesler. 2020. More than “If Time Allows” the role of ethics in AI education. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. 272–278.
[49]
Timnit Gebru. 2021. Hierarchy of Knowledge in Machine Learning and Related Fields and Its Consequences. https://www.youtube.com/watch?v=OL3DowBM9uc
[50]
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2021. Datasheets for datasets. Commun. ACM 64, 12 (2021), 86–92.
[51]
Henry A Giroux. 2007. Border crossings: Cultural workers and the politics of education. Routledge.
[52]
Henry A Giroux. 2020. On critical pedagogy. Bloomsbury Publishing.
[53]
Charles Goodwin. 2015. Professional vision. In Aufmerksamkeit: Geschichte-Theorie-Empirie. Springer, 387–425.
[54]
Google. 2024. Responsible AI Practices. Retrieved Jan 2024 from https://ai.google/responsibility/responsible-ai-practices/
[55]
Ben Green. 2021. The contestation of tech ethics: A sociotechnical approach to technology ethics in practice. Journal of Social Computing 2, 3 (2021), 209–225.
[56]
Barbara J Grosz, David Gray Grant, Kate Vredenburgh, Jeff Behrends, Lily Hu, Alison Simmons, and Jim Waldo. 2019. Embedded EthiCS: integrating ethics across CS education. Commun. ACM 62, 8 (2019), 54–61.
[57]
Berndt Hamm and John M Frymire. 1999. Normative Centering in the Fifteenth and Sixteenth Centuries: Observations on Religiosity, Theology, and Iconology. Journal of Early Modern History 3, 3 (1999), 307–354.
[58]
Alex Hanna and Tina M Park. 2020. Against scale: Provocations and resistances to scale thinking. arXiv preprint arXiv:2010.08850 (2020).
[59]
Christina Harrington, Sheena Erete, and Anne Marie Piper. 2019. Deconstructing community-based collaborative design: Towards more equitable participatory design engagements. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (2019), 1–25.
[60]
Amy Heger, Samir Passi, and Mihaela Vorvoreanu. 2022. All the tools, none of the motivation: Organizational culture and barriers to responsible AI work. (2022).
[61]
Zoë Hitzig. 2020. The normative gap: mechanism design and ideal theories of justice. Economics & Philosophy 36, 3 (2020), 407–434.
[62]
Shlomi Hod, Karni Chagal-Feferkorn, Niva Elkin-Koren, and Avigdor Gal. 2022. Data science meets law. Commun. ACM 65, 2 (2022), 35–39.
[63]
Anna Lauren Hoffmann and Katherine Alejandra Cross. 2021. Teaching Data Ethics: Foundations and Possibilities from Engineering and Computer Science Ethics Education. (2021).
[64]
Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudik, and Hanna Wallach. 2019. Improving fairness in machine learning systems: What do industry practitioners need?. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–16.
[65]
Bell Hooks. 2014. Teaching to transgress. Routledge.
[66]
Diane Horton, David Liu, Sheila A McIlraith, and Nina Wang. 2023. Is More Better When Embedding Ethics in CS Courses?. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1. 652–658.
[67]
White House. 2023. Blueprint for an AI Bill of Rights. White House. Retrieved May 2023 from https://www.whitehouse.gov/ostp/ai-bill-of-rights/
[68]
White House. 2023. Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. White House. Retrieved Novemeber 2023 from https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/
[69]
Iris Howley, Darakhshan Mir, and Evan Peck. 2022. Integrating AI ethics across the computing curriculum. In The Ethics of Artificial Intelligence in Education. Routledge, 255–270.
[70]
Anna Jobin, Marcello Ienca, and Effy Vayena. 2019. The global landscape of AI ethics guidelines. Nature Machine Intelligence 1, 9 (2019), 389–399.
[71]
Kaggle. 2021. State of Data Science and Machine Learning. (2021). https://www.kaggle.com/kaggle-survey-2021
[72]
Miryung Kim, Thomas Zimmermann, Robert DeLine, and Andrew Begel. 2017. Data scientists in software teams: State of the art and challenges. IEEE Transactions on Software Engineering 44, 11 (2017), 1024–1038.
[73]
Amy J. Ko, Anne Beitlers, Brett Wortzman, Matt Davidson, Alannah Oleson, Mara Kirdani-Ryan, Stefania Druga, and Jayne Everson. 2023. Critically Conscious Computing: Methods for Secondary Education. https://criticallyconsciouscomputing.org’
[74]
Amy J Ko, Alannah Oleson, Neil Ryan, Yim Register, Benjamin Xie, Mina Tari, Matthew Davidson, Stefania Druga, and Dastyni Loksa. 2020. It is time for more critical CS education. Commun. ACM 63, 11 (2020), 31–33.
[75]
Ari Krakowski, Eric Greenwald, Timothy Hurt, Brandie Nonnecke, and Matthew Cannady. 2022. Authentic Integration of Ethics and AI through Sociotechnical, Problem-Based Learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 12774–12782.
[76]
Sean Kross and Philip J Guo. 2019. Practitioners teaching data science in industry and academia: Expectations, workflows, and challenges. In Proceedings of the 2019 CHI conference on human factors in computing systems. 1–14.
[77]
David F Labaree. 1997. Public goods, private goods: The American struggle over educational goals. American educational research journal 34, 1 (1997), 39–81.
[78]
Christopher A Le Dantec, Erika Shehan Poole, and Susan P Wyche. 2009. Values as lived experience: evolving value sensitive design in support of value discovery. In Proceedings of the SIGCHI conference on human factors in computing systems. 1141–1150.
[79]
Michelle Seng Ah Lee and Jat Singh. 2021. The landscape and gaps in open source fairness toolkits. In Proceedings of the 2021 CHI conference on human factors in computing systems. 1–13.
[80]
Armanda Lewis and Julia Stoyanovich. 2021. Teaching responsible data science: Charting new pedagogical territory. International Journal of Artificial Intelligence in Education (2021), 1–25.
[81]
Michael Madaio, Lisa Egede, Hariharan Subramonyam, Jennifer Wortman Vaughan, and Hanna Wallach. 2022. Assessing the Fairness of AI Systems: AI Practitioners’ Processes, Challenges, and Needs for Support. Proceedings of the ACM on Human-Computer Interaction 6, CSCW1 (2022), 1–26.
[82]
Michael A Madaio, Luke Stark, Jennifer Wortman Vaughan, and Hanna Wallach. 2020. Co-designing checklists to understand organizational challenges and opportunities around fairness in ai. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–14.
[83]
James W Malazita and Korryn Resetar. 2019. Infrastructures of abstraction: how computer science education produces anti-political subjects. Digital Creativity 30, 4 (2019), 300–312.
[84]
Maya Malik and Momin M Malik. 2021. Critical technical awakenings. Journal of Social Computing 2, 4 (2021), 365–384.
[85]
Sean McGregor. 2020. Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. arXiv 2011.08512 (2020). http://arxiv.org/abs/2011.08512
[86]
Jacob Metcalf, Emanuel Moss, 2019. Owning ethics: Corporate logics, silicon valley, and the institutionalization of ethics. Social Research: An International Quarterly 86, 2 (2019), 449–476.
[87]
Milagros Miceli, Julian Posada, and Tianling Yang. 2022. Studying up machine learning data: Why talk about bias when we mean power?Proceedings of the ACM on Human-Computer Interaction 6, GROUP (2022), 1–14.
[88]
Milagros Miceli, Martin Schuessler, and Tianling Yang. 2020. Between subjectivity and imposition: Power dynamics in data annotation for computer vision. Proceedings of the ACM on Human-Computer Interaction 4, CSCW2 (2020), 1–25.
[89]
Jessica K Miller, Batya Friedman, Gavin Jancke, and Brian Gill. 2007. Value tensions in design: the value sensitive design, development, and appropriation of a corporation’s groupware system. In Proceedings of the 2007 ACM International Conference on Supporting Group Work. 281–290.
[90]
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019. Model cards for model reporting. In Proceedings of the conference on fairness, accountability, and transparency. 220–229.
[91]
Jessica Morley, Luciano Floridi, Libby Kinsey, and Anat Elhalal. 2020. From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices. Sci. Eng. Ethics 26, 4 (Aug. 2020), 2141–2168.
[92]
Evgeny Morozov. 2013. To save everything, click here: The folly of technological solutionism. PublicAffairs.
[93]
Omidyar Network. 2018. Ethical OS Toolkit. Retrieved Jan 2024 from https://rri-tools.eu/-/ethical-os-toolkit#: :text=The%20Ethical%20Operating%20System%20can,minimize%20technical%20and%20reputational%20risks.
[94]
Alexi Orchard and David Radke. 2023. An Analysis of Engineering Students’ Responses to an AI Ethics Scenario. Proceedings of the AAAI Conference on Artificial Intelligence 37, 13 (2023), 5834–15842.
[95]
Alicia Parrish, Hannah Rose Kirk, Jessica Quaye, Charvi Rastogi, Max Bartolo, Oana Inel, Juan Ciro, Rafael Mosquera, Addison Howard, Will Cukierski, 2023. Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models. arXiv preprint arXiv:2305.14384 (2023).
[96]
Justin Petelka, Megan Finn, Franziska Roesner, and Katie Shilton. 2022. Principles matter: integrating an ethics intervention into a computer security course. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education-Volume 1. 474–480.
[97]
Ajit G. Pillai, A. Baki Kocaballi, Tuck Wah Leong, Rafael A. Calvo, Nassim Parvin, Katie Shilton, Jenny Waycott, Casey Fiesler, John C. Havens, and Naseem Ahmadpour. 2021. Co-designing resources for ethics education in HCI. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. 1–5.
[98]
Peter Pirolli and Stuart Card. 1999. Information foraging.Psychological review 106, 4 (1999), 643.
[99]
Neil Postman. 1971. Teaching as a subversive activity: A no-holds-barred assault on outdated teaching methods-with dramatic and practical proposals on how education can be made relevant to today’s world. Delta.
[100]
Mahima Pushkarna, Andrew Zaldivar, and Oddur Kjartansson. 2022. Data cards: Purposeful and transparent dataset documentation for responsible ai. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. 1776–1826.
[101]
Inioluwa Deborah Raji, Morgan Klaus Scheuerman, and Razvan Amironesei. 2021. You can’t sit with us: Exclusionary pedagogy in ai ethics education. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency. 515–525.
[102]
Bogdana Rakova, Jingying Yang, Henriette Cramer, and Rumman Chowdhury. 2021. Where responsible AI meets reality: Practitioner perspectives on enablers for shifting organizational practices. Proceedings of the ACM on Human-Computer Interaction 5, CSCW1 (2021), 1–23.
[103]
Stephen Rea, Qin Zhu, Dean Nieusma, Kylee Shiekh, and Tom Williams. 2021. Cultivating Ethical Engineers in the Age of AI and Robotics: An Educational Cultures Perspective. In IEEE International Symposium on Technology and Society.
[104]
Rashida Richardson. 2021. Defining and demystifying automated decision systems. Md. L. Rev. 81 (2021), 785.
[105]
Negar Rostamzadeh, Diana Mincu, Subhrajit Roy, Andrew Smart, Lauren Wilcox, Mahima Pushkarna, Jessica Schrouff, Razvan Amironesei, Nyalleng Moorosi, and Katherine Heller. 2022. Healthsheet: Development of a Transparency Artifact for Health Datasets. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT ’22). Association for Computing Machinery, New York, NY, USA, 1943–1961. https://doi.org/10.1145/3531146.3533239
[106]
Negar Rostamzadeh, Diana Mincu, Subhrajit Roy, Andrew Smart, Lauren Wilcox, Mahima Pushkarna, Jessica Schrouff, Razvan Amironesei, Nyalleng Moorosi, and Katherine Heller. 2022. Healthsheet: development of a transparency artifact for health datasets. In 2022 ACM Conference on Fairness, Accountability, and Transparency. 1943–1961.
[107]
Jeffrey Saltz, Michael Skirpan, Casey Fiesler, Micha Gorelick, Tom Yeh, Robert Heckman, Neil Dewar, and Nathan Beard. 2019. Integrating ethics within machine learning courses. ACM Transactions on Computing Education (TOCE) 19, 4 (2019), 1–26.
[108]
Nithya Sambasivan, Erin Arnesen, Ben Hutchinson, Tulsee Doshi, and Vinodkumar Prabhakaran. 2021. Re-imagining algorithmic fairness in india and beyond. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency. 315–328.
[109]
Lara L Schenck and Betsy DiSalvo. 2023. From Data Work to Data Science: Getting Past the Gatekeepers. In Proceedings of the 2023 ACM Conference on International Computing Education Research-Volume 2. 1–2.
[110]
Campbell F Scribner. 2016. The fight for local control: Schools, suburbs, and American democracy. Cornell University Press.
[111]
Andrew D Selbst, Danah Boyd, Sorelle A Friedler, Suresh Venkatasubramanian, and Janet Vertesi. 2019. Fairness and abstraction in sociotechnical systems. In Proceedings of the conference on fairness, accountability, and transparency. 59–68.
[112]
Akseli Seppälä, Teemu Birkstedt, and Matti Mäntymäki. 2021. From ethical AI principles to governed AI. In Proceedings of the 42nd International Conference on Information Systems (ICIS2021).
[113]
Ben Rydal Shapiro, Emma Lovegall, Amanda Meng, Jason Borenstein, and Ellen Zegura. 2021. Using role-play to scale the integration of ethics across the Computer Science curriculum. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education. 1034–1040.
[114]
Ben Rydal Shapiro, Amanda Meng, Cody O’Donnell, Charlotte Lou, Edwin Zhao, Bianca Dankwa, and Andrew Hostetler. 2020. Re-Shape: A method to teach data ethics for data science education. In Proceedings of the 2020 CHI conference on human factors in computing systems. 1–13.
[115]
Renee Shelby, Shalaleh Rismani, Kathryn Henne, AJung Moon, Negar Rostamzadeh, Paul Nicholas, N’Mah Yilla-Akbari, Jess Gallegos, Andrew Smart, Emilio Garcia, 2023. Sociotechnical harms of algorithmic systems: Scoping a taxonomy for harm reduction. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society. 723–741.
[116]
Hong Shen, Wesley H Deng, Aditi Chattopadhyay, Zhiwei Steven Wu, Xu Wang, and Haiyi Zhu. 2021. Value cards: An educational toolkit for teaching social impacts of machine learning through deliberation. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency. 850–861.
[117]
Susan S Silbey. 2009. Taming Prometheus: Talk about safety and culture. Annual Review of Sociology 35 (2009), 341–369.
[118]
Mario Luis Small and Jessica McCrory Calarco. 2022. Qualitative literacy: A guide to evaluating ethnographic and interview research. Univ of California Press.
[119]
Jessie J Smith, Blakeley H Payne, Shamika Klassen, Dylan Thomas Doyle, and Casey Fiesler. 2023. Incorporating Ethics in Computing Courses: Barriers, Support, and Perspectives from Educators. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1. 367–373.
[120]
Robert Soden, Austin Toombs, and Michaelanne Thomas. 2024. Evaluating Interpretive Research in HCI. Interactions 31, 1 (2024), 38–42.
[121]
Katta Spiel, Oliver L Haimson, and Danielle Lottridge. 2019. How to do better with gender on surveys: a guide for HCI researchers. Interactions 26, 4 (2019), 62–65.
[122]
Ramya Srinivasan, Remi Denton, Jordan Famularo, Negar Rostamzadeh, Fernando Diaz, and Beth Coleman. 2021. Artsheets for art datasets. In Thirty-fifth conference on neural information processing systems datasets and benchmarks track (round 2).
[123]
Lucy Suchman. 2002. Located accountabilities in technology production. Scandinavian journal of information systems 14, 2 (2002), 7.
[124]
Lucy Suchman. 2021. Border thinking about anthropologies/designs. Designs and Anthropologies (2021), 17–33.
[125]
Lucy Suchman. 2023. The uncontroversial ‘thingness’ of AI. Big Data & Society 10, 2 (2023), 20539517231206794.
[126]
Elham Tabassi. 2023. Artificial Intelligence Risk Management Framework (AI RMF 1.0). (2023). https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936225
[127]
Anna Lowenhaupt Tsing. 2012. On nonscalability: The living world is not amenable to precision-nested scales. Common knowledge 18, 3 (2012), 505–524.
[128]
Emily Tucker. 2022. Artifice and intelligence. Center on Privacy & Technology at Georgetown Law Blog (2022).
[129]
Sherry Turkle and Seymour Papert. 1990. Epistemological pluralism: Styles and voices within the computer culture. Signs: Journal of women in culture and society 16, 1 (1990), 128–157.
[130]
Princeton University. 2019. Dialogues on AI and Ethics Case Studies. Retrieved Jan 2024 from https://aiethics.princeton.edu/case-studies/
[131]
Ville Vakkuri, Marianna Jantunen, Erika Halme, Kai-Kristian Kemell, Anh Nguyen-Duc, Tommi Mikkonen, and Pekka Abrahamsson. 2021. Time for AI (ethics) maturity model is now. arXiv preprint arXiv:2101.12701 (2021).
[132]
Rama Adithya Varanasi and Nitesh Goyal. 2023. “It is currently hodgepodge”: Examining AI/ML Practitioners’ Challenges during Co-production of Responsible AI Values. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–17.
[133]
Mihaela Vorvoreanu. 2023. Create Effective and Responsible AI User Experiences with The Human-AI Experience (HAX) Toolkit. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI EA ’23, Article 534). Association for Computing Machinery, New York, NY, USA, 1–2.
[134]
Mihaela Vorvoreanu, Amy Heger, Samir Passi, Shipi Dhanorkar, Zoe Kahn, and Ruotong Wang. 2023. Responsible AI Maturity Model. Technical Report MSR-TR-2023-26. Microsoft. https://www.microsoft.com/en-us/research/publication/responsible-ai-maturity-model/
[135]
Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023. Designing responsible ai: Adaptations of ux practice to meet responsible ai challenges. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–16.
[136]
Hilde Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, and Michael Madaio. 2023. Fairlearn: Assessing and Improving Fairness of AI Systems. Journal of Machine Learning Research 24, 257 (2023), 1–8.
[137]
Hilde Jacoba Petronella Weerts and Mykola Pechenizkiy. 2022. Teaching responsible machine learning to engineers. In Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop. PMLR, 40–45.
[138]
David Gray Widder and Dawn Nafus. 2023. Dislocated accountabilities in the “AI supply chain”: Modularity and developers’ notions of responsibility. Big Data & Society 10, 1 (2023), 20539517231177620.
[139]
David Gray Widder, Derrick Zhen, Laura Dabbish, and James Herbsleb. 2023. It’s about power: What ethical concerns do software engineers have, and what do they (feel they can) do about them?. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. 467–479.
[140]
Richmond Y Wong. 2021. Tactics of Soft Resistance in User Experience Professionals’ Values Work. Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021), 1–28.
[141]
Richmond Y Wong, Michael A Madaio, and Nick Merrill. 2023. Seeing like a toolkit: How toolkits envision the work of AI ethics. Proceedings of the ACM on Human-Computer Interaction 7, CSCW1 (2023), 1–27.
[142]
Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023. Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People+ AI Guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–13.
[143]
Cass Zegura, Ben Rydal Shapiro, Robert MacDonald, Jason Borenstein, and Ellen Zegura. 2023. “Moment to Moment”: A Situated View of Teaching Ethics from the Perspective of Computing Ethics Teaching Assistants. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–15.
[144]
Jia Zhu, Stephanie J Lunn, and Monique Ross. 2023. Characterizing Women’s Alternative Pathways to a Computing Career Using Content Analysis. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1. 158–164.
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June 2024
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ISBN:9798400704505
DOI:10.1145/3630106
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