Constructing AI: Examining how AI is shaped by data, models and people
Keywords:Artificial Intelligence, Consent, Collection, Diversity, Privacy, Surveillance Capitalism
Artificial Intelligence (AI) is a technology that is quickly becoming part of our digital infrastructure and woven into aspects of daily life. AI has the potential to impact society in many positive ways. However, there are numerous examples of AI systems that are operating in ways that are harmful, unjust and discriminatory. AI systems are constructs of the choices made in their design. They exist within a socio-cultural context that reflects the data used in their training, the design of their mathematical models and the values of their creators. If we want to build AI systems that benefit society, we need to change how we construct AI.
Ahmed, W., Bath, P. & Demartini, G. Chapter 4 Using Twitter as a Data Source: An Overview of Ethical, Legal, and Methodological Challenges. In: Woodfield, K., (ed.) The Ethics of Online Research. Advances in Research Ethics and Integrity (2). Emerald, pp. 79-107. ISBN 978-1-78714-486-6, 2017.
Bajarin, T. Why it matters that IBM has abandoned its facial recognition technology. Forbes, 2020. Retrieved from - https://www.forbes.com/sites/timbajarin/2020/06/18/why-it-matters-that-ibm-has-abandoned-its-facial-recognition-technology/#f66ec3fafaf3
Budds, D. Exclusive: Ideo’s Plan To Stage An AI Revolution, 2017. Retrieved December 9, 2019, from Fast Company website: https://www.fastcompany.com/90147010/exclusive-ideos-plan-to-stage-an-ai-revolution
Burdick, A. The A.I. “Gaydar” Study and the Real Dangers of Big Data. The New Yorker, 2017. Retrieved from https://www.newyorker.com/news/daily-comment/the-ai-gaydar-study-and-the-real-dangers-of-big-data
Cassell, J. Genderizing HCI, 2001. Retrieved from https://pdfs.semanticscholar.org/4810/c28fe3523b52bd39b1eeb3b6225ab2145fa7.pdf
Dastin, J. Amazon scraps secret AI recruiting tool that showed bias against women. Reuters, 2018. Retrieved from - https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G
Domingos, P. The Master Algorithm: How the quest for the ultimate learning machine will remake our world. New York, NY: Basic Books, 2015.
Fabbri, A., Lai, A., Grundy, Q., Bero, L. A. The influence of industry sponsorship on the research agenda: A scoping review. American Journal of Public Health. 108 (11), e9-e16, 2018.
Gomes de Andrade, N., Pawson, D., Muriello, D. et al. Ethics and Artificial Intelligence: Suicide Prevention on Facebook. Philos. Technol. 31, 669–684, 2018. doi:10.1007/s13347-018-0336-0
Harari, Y.N. The Data Religion. In Homo Deus A Brief History of Tomorrow (pp 428-462). Toronto, ON. Penguin Random House Canada, 2015.
Heffetz, O., & Ligett, K. Privacy and Data-Based Research. The Journal of Economic Perspectives: A Journal of the American Economic Association, 28(2), 75–98, 2014. https://doi.org/10.1257/jep.28.2.75
Henle, T., Matthews, G. J., & Harel, O. Data Confidentiality. In A. Levy, S. Goring, C. Gatsonis, B. Sobolev, E. van Ginneken, & R. Busse (Eds.), Health Services Evaluation (pp. 717–731), 2019. https://doi.org/10.1007/978-1-4939-8715-3_28
Kitchin, Rob. The Data Revolution: Big Data, Open Data, Data Infrastructures & Their Consequences. Sage, UK, 2017.
Knight, W. An AI pioneer wants his algorithms to understand the why. Wired, 2019. Retrieved from - https://www.wired.com/story/ai-pioneer-algorithms-understand-why/
Kugler, Matthew B., From Identification to Identity Theft: Public Perceptions of Biometric Privacy Harms. U.C. Irvine Law Review. (Forthcoming), 2018. Available at SSRN: https://ssrn.com/abstract=3289850 or http://dx.doi.org/10.2139/ssrn.3289850
Lohr, S. Data-ism: The Revolution Transforming Decision Making, Consumer Behaviour And Almost Everything Else. New York, NY: Harper Collins, 2015.
Maani, N., & Galea, S. COVID-19 and Underinvestment in the Public Health Infrastructure of the United States. The Milbank Quarterly, 98(2), 250–259, 2020. https://doi.org/10.1111/1468-0009.12463
Mantha, Y. and Kiser, G. Global AI Talent Report, 2019. Retrieved from https://jfgagne.ai/talent-2019/
Marcus, G. and Davis, E. Rebooting AI Building Artificial Intelligence We Can Trust. New York, NY: Penguin Random House, 2019.
Miltner, K. Girls who coded gender in twentieth century U.K. and U.S. computing. [Review of the books Programmed Inequality: How Britain Discarded Women Technologists and Lost its Edge by Marie Hicks, Recoding Gender: Women’s changing participation in computing by Janet Abbatte and The Computer Boys Take Over: Programmers and the Politics of Technical Expertise by Nathan Ensmenger]. Science, Technology & Human Values. 44(1), 161-176, 2019.
Noble, S. U. Algorithms of Oppression. New York, NY: New York University Press, 2018.
O’Neil, C. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown Publishers, 2016.
Richards, N. M. and Hartzog, W. The Pathologies of Digital Consent. Washington University Law Review, 2019. Retrieved from https://ssrn.com/abstract=3370433
Semuels, A. The Internet Is Enabling a New Kind of Poorly Paid Hell. The Atlantic, 2018. Retrieved from - https://www.theatlantic.com/business/archive/2018/01/amazon-mechanical-turk/551192/
Shilton, K. Anticipatory ethics for a future Internet: analyzing values during the design of an Internet infrastructure. Science and Engineering Ethics, 21(1), 1–18, 2015. https://doi.org/10.1007/s11948-013-9510-z
Slaughter, S., Archerd, C. J., Campbell, T. I. D. Boundaries and Quandaries: How Professors Negotiate Market Relations. The Review of Higher Education. 28(1), 129-165, 2004.
Smith, C. S. Building a World Where Data Privacy Exists Online. The New York Times, 2019. Retrieved from https://www.nytimes.com/2019/11/19/technology/artificial-intelligence-dawn-song.html
Solove, D. J. Nothing to Hide: The False Trade-off between Privacy and Security. New Haven, CT: Yale University Press, 2013.
Strubell, E., Ganesh, A., & McCallum, A. Energy and Policy Considerations for Deep Learning in NLP, 2019. Retrieved from http://arxiv.org/abs/1906.02243
Wang, Y., & Kosinski, M. Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. Journal of personality and social psychology, 114(2), 246, 2018.
West, S.M. Whittaker, M and Crawford, K. Discriminating systems: Gender, race and power in AI. AI Now Institute, 2019. Retrieved from https://ainowinstittue.org/discriminatingsystems.html
Wittkower, D.E. Disaffordances and dysaffordances in code. Paper presented at AoIR 2017: The 18th Annual Conference of the Association of Internet Researchers. Tartu, Estonia: AoIR, 2017. Retrieved from - http://spir.aoir.org
Zang, J., Dummit, K., Graves, J., Lisker, P., & Sweeney, A. L. Who Knows What About Me? A Survey of Behind the Scenes Personal Data Sharing to Third Parties by Mobile Apps. Technology Science, 2015. Retrieved from https://techscience.org/a/2015103001.pdf
Zerilli, J., Knott, A., Maclaurin, J., & Gavaghan, C. Transparency in Algorithmic and Human Decision-Making: Is There a Double Standard? Philosophy & Technology, 2018. https://doi.org/10.1007/s13347-018-0330-6
Zuboff, S. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. New York: PublicAffairs, 2019.