Course 1. Data governance, Ethics and Design

With cases on AI and Smart Cities (MSc level)

– Alison Powell, LSE, 2018

Issues in Data Governance

Overview

This lecture looks at modes of organisation of data and the consequences of employing different models of data organisation and access. Examining the promise of data commons alongside other proposals to organise data in ‘collaboratories’ or cooperatives, this lecture examines how politics and power appear in discussions about the organisation and use of data, particularly conflicting ideas of data sovereignty.

Required reading:

  1. British   Columbia  First   Nations   Data   Governance   Initiative. http://www.bcfndgi.com/ History and Future
  2. Concept Paper
  3. Frischmann, B. M., Madison, M. J., & Strandburg, K. J. (Eds.). (2014). Governing knowledge commons. Oxford University Press. Introduction.
  4. Verlhurst, S, Young, A. and P. Srinivasen. (2016) An Introduction to Data Collaboratives. Creating Public Value By Exchanging Data.


Recommended reading:

  1. Balestrini, M., Rogers, Y., Hassan, C., Creus, J., King, M., & Marshall, P. (2017, May). A city in common: a framework to orchestrate large-scale citizen engagement around urban issues. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 2282- 2294). ACM.
  2. Drake, W. Cerf, V. and W. Kleinwächter. (2016) Internet Fragmentation: An Overview. Future of the Internet Initiative White Paper: World Economic Forum.
  3. Graham, Mark, and Laura Mann. “Imagining a silicon savannah? Technological and conceptual connectivity in Kenya’s BPO and software development sectors.” The Electronic Journal of Information Systems in Developing Countries 56.1 (2013): 1-19.
  4. Taylor, L and R. Schroeder – Is Bigger Better? The Emergence of Big Data as a Tool for International Development Policy. GeoJournalAugust 2015, Volume 80, Issue 4, pp. 503–518.
  5. Polatin-Reuben, D., & Wright, J. (2014, July). An Internet with BRICS Characteristics: Data Sovereignty and the Balkanisation of the Internet. In FOCI.
  6. Kukutai, T. and J. Taylor (2017). Indigenous Data Sovereignty: Towards an Agenda. Associated website: https://www.temanararaunga.maori.nz/
  7. Sandra Wachter, Brent Mittelstadt, Luciano Floridi; Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation, International Data Privacy Law, Volume 7, Issue 2, 1 May 2017, Pages 76–99, https://doi.org/10.1093/idpl/ipx005

Data System Design, within and beyond Ethics?

Overview

We will explore questions of design in relation to data systems that collect, curate and calculate data, and investigate their social and cultural implications. It includes a discussion of two research projects, the Understanding Automated Decisions project and the Virt-EU project exploring ethics, values and the Internet of Things. Where should considerations of ethical and social impact be placed with design or governance processes? How should these issues be addressed within workplace contexts? What are the possibilities for changing design practices based on an understanding of datafication?

Required reading:

  1. Metcalf, J., & Crawford, K. (2016). Where are human subjects in big data research? The emerging ethics divide. Big Data & Society, 3(1), 1-14.
  2. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1-21.
  3. Pink, Ruckstein and Willim (2018) Broken Data: Conceptualizing Data in an Emerging World. Big Data and Society January 2018.

Recommended reading:

  1. Aradau, C., & Blanke, T. (2016). Politics of prediction Security and the time/space of governmentality in the age of big data. European Journal of Social Theory, 1368431016667623.
  2. Dijck, J. van (2014). Datafication, Dataism and Dataveillance: Big Data between Scientific Paradigm and Ideology. Surveillance & Society, 12(2), pp. 197–208.
  3. Esposti,    S.   (2014). When   Big    Data    Meets    Dataveillance:    The   Hidden    Side   of Analytics. Surveillance & Society, 12(2), pp. 209-225.
  4. Building   digital    trust:    The   role    of    data   ethics   in    the    digital    age   (Accenture Labs) https://www.accenture.com/t00010101T000000Z__w         /gb-en/_acnmedia/PDF- 22/Accenture-Data-Ethics-POV-WEB.pdf#zoom=50
  5. Mantelero, Alessandro (2018) AI and Big Data: A blueprint for a human rights, ethical and social impact assessment. Computer Law and Security Review.
  6. DOI: https://doi.org/10.1016/j.clsr.2018.05.017

Also look at: MIT’s Moral Maze https://www.media.mit.edu/projects/moral-machine/overview

Cultures of AI

Overview

We will examine the socio-cultural features associated with the development of AI technologies over the past four decades, and also ask questions about the extent to which we should pay attention to the materiality of our technical systems. What happens when outcomes are outside of the features that we can control? When sensed data takes on a life of its own? Our reading focuses on the ways that certain patterns of scientific investigation produce particular assumptions about technology design.

Required reading:

  1. Brunton, Finn, and Gabriella Coleman (2014). “Closer to the Metal”. In: Gillespie et al., Media Technologies: Essays on Communication, Materiality, and Society, 77.
  2. Ensmenger, Nathan. “Is Chess the Drosophila of Artificial Intelligence? A Social History of an Algorithm.” Social Studies of Science 42, no. 1 (2012): 5–30.
  3. Schüll, Natasha Dow. “Mapping the Machine Zone” in Addiction By Design: Machine Gambling in Las Vegas, 1–27. Princeton: Princeton University Press, 2012.


Recommended:

  1. Beer D. (2009) ‘Power through the algorithm? Participatory web cultures and the technological
  2. unconscious’, New Media & Society, 11(6), pp. 985–1002.
  3. Chun, Wendy Hui Kyong. Control and Freedom: Power and Paranoia in the Age of Fiber Optics.
  4. Cambridge, MA: MIT Press, 2006.
  5. Cohn, Marisa. “” Lifetime Issues”: Temporal Relations of Design and Maintenance.” continent. 6, no. 1 (2017): 4-12.
  6. Daipha, Phaedra. Masters of Uncertainty: Weather Forecasters and the Quest for Ground Truth.Chicago: University of Chicago Press, 2015.
  7. Ensmenger, Nathan. “Software as History Embodied.” IEEE Annals of the History of Computing 31, no. 1 (2009): 86–88.
  8. Galloway, Alexander R. Protocol: How Control Exists after Decentralization. Cambridge, MA: MIT Press, 2004.
  9. Galloway, Alexander R., and Eugene Thacker. The Exploit: A Theory of Networks. Minneapolis: University of Minnesota Press, 2007.
  10. Gillespie, Tarleton. Wired Shut: Copyright and the Shape of Digital Culture. Cambridge, MA: MIT Press, 2007.
  11. Jain, Sarah S. Lochlann. Injury: The politics of product design and safety law in the United States. Princeton, NJ: Princeton University Press, 2006.
  12. Kennedy, Devin. “The Machine in the Market: Computers and the Infrastructure of Price at the New York Stock Exchange, 1965–1975.” Social studies of science 47, no. 6 (2017): 888–917.
  13. Kohler, Robert E. Lords of the fly: Drosophila genetics and the experimental life. Chicago: University of Chicago Press, 1994.
  14. Lessig, Lawrence. Code: Version 2.0. New York: Basic Books, 2006.
  15. Mackenzie, Donald A. Inventing Accuracy: A Historical Sociology of Nuclear Missile Guidance.Cambridge, MA: MIT Press, 1990.
  16. Mager, Astrid (2012) Alogrithmic Ideology: How capitalist society shapes search  engines Information, Communication and Society Volume 15, Issue 5, June 2012, pages 769- 787.
  17. Mahoney, Michael S. “Finding a history for software engineering.” IEEE Annals of the History of Computing 26, no. 1 (2004): 8–19.
  18. Shell, Hanna Rose. Hide and Seek: Camouflage, Photography, and the Media of Reconnaissance. New York: Zone Books, 2012.
  19. Vertesi, Janet. Seeing Like a Rover: How Robots, Teams, and Images Craft Knowledge of Mars. Chicago: University of Chicago Press, 2015.

Optimizing Space and Civic Participation: Smart Cities

Overview

Can civic life be made more optimal using data, sensors and ‘ambient’ urban media technologies? We look at the promises and critiques of the idea of optimized social life (including Internet of Things) in the case of the smart city.

Required:

  1. Gabrys, Jennifer (2016) Program Earth. University of Minnesota Press.
  2. Sadowski, J., Pasquale, F. (2015). The Spectrum of Control: A Social Theory of the Smart City. First Monday, 20(7).

Recommended:

  1. Gordon, E. and de Souza e Silva, A. (2011). Chapter 2: Mobile annotation. In: Net Locality Why Location Matters in a Networked World. Wiley-Blackwell, pp. 40-58.
  2. Graham, Stephen, and Simon Marvin. Splintering Urbanism: Networked Infrastructures, Technological Mobilities and the Urban Condition. London; New York: Routledge, 2001.
  3. Kitchin, R. (2013). The Real-Time City? Big Data and Smart Urbanism. SSRN Scholarly Paper. Rochester, NY: Social Science.
  4. Mattern, Shannon (2017) “A City Is Not a Computer,” Places Journal, February 2017. Accessed 23 Nov 2018. https://doi.org/10.22269/170207
  5. O’Brien, Daniel (2018) The Urban Commons: How data and technology can rebuild our communities.
  6. Powell, Alison (2014) ‘Datafication’, transparency, and good governance of the data city. In: O’Hara, Kieron and Nguyen, Carolyn and Haynes, Peter, (eds.) Digital Enlightenment Yearbook 2014: Social Networks and Social Machines, Surveillance and Empowerment. ISO Press Ebooks, pp. 215-224.
  7. Ratti, Carl and Matthew Claudel. The City of Tomorrow: Sensors, Networks, Hackers, and the Future of Urban Life. New Haven: Yale University Press.