The (in)credibility of algorithmic models to non-experts

References

• Barocas, S., & Selbst, A. (2016). Big data’s disparate impact. California Law Review, 104(1), 671–729. www.jstor.org/stable/24758720
   Google Scholar
• Bissell, C., Dillon, C., & Mansnerus, E. (2012). The inner world of models and its epistemic diversity: Infectious disease and climate modelling. In G. Gramelsberger & E. Mansnerus (Eds.), Ways of thinking, ways of seeing. Automation, collaboration, & e-services (pp. 167–195). Springer. https://doi.org/https://doi.org/10.1007/978-3-642-25209-9
   Google Scholar
• Boulanger, P. M. P.-M., & Bréchet, T. (2005). Models for policy in sustainable development: The state of the art and perspectives for research. Ecological Economics, 55(3), 337–350. https://doi.org/https://doi.org/10.1016/j.ecolecon.2005.07.033
   Web of Science ®Google Scholar
• Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. https://doi.org/https://doi.org/10.1080/1369118X.2012.678878
   Web of Science ®Google Scholar
• Cardon, D., Cointet, J. P., & Mazieres, A. (2018). Neurons spike back: The invention of inductive machines and the artificial intelligence controversy.
   Google Scholar
• Cash, D., & Clark, W. C. (2001). From science to policy: Assessing the assessment process. John F. Kennedy School of Government Faculty Research Working Papers Series.
   Google Scholar
• Christin, A. (2017). Algorithms in practice: Comparing web journalism and criminal justice. Big Data & Society, 4(2), 1–14. https://doi.org/https://doi.org/10.1177/2053951717718855
   Web of Science ®Google Scholar
• Diakopoulos, N., & Friedler, S. (2016). How to hold algorithms accountable. MIT Technology Review, 17(11).
   Google Scholar
• Diez, E., & McIntosh, B. S. (2011). Organisational drivers for, constraints on and impacts of decision and information support tool use in desertification policy and management. Environmental Modelling & Software, 26(3), 317–327. https://doi.org/https://doi.org/10.1016/j.envsoft.2010.04.003
   Web of Science ®Google Scholar
• Dourish, P. (2016). Algorithms and their others: Algorithmic culture in context. Big Data & Society, 3(2), 1–15. https://doi.org/https://doi.org/10.1177/2053951716665128
   Web of Science ®Google Scholar
• Espeland, W. N., & Stevens, M. L. (2008). A sociology of quantification. European Journal of Sociology, 49(3), 401–436. https://doi.org/https://doi.org/10.1017/S0003975609000150
   Web of Science ®Google Scholar
• Floridi, L., & Taddeo, M. (2016). What is data ethics? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083), 20160360. https://doi.org/https://doi.org/10.1098/rsta.2016.0360
   PubMed Web of Science ®Google Scholar
• Gilbert, N., & Mulkay, M. (1984). Opening Pandorra’s box. Cambridge University Press.
   Google Scholar
• Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a “right to explanation”. AI Magazine, 38(3), 50–57. https://doi.org/https://doi.org/10.1609/aimag.v38i3.2741
   Web of Science ®Google Scholar
• Goodwin, P., Fildes, R., Lawrence, M., & Nikolopoulos, K. (2007). The process of using a forecasting support system. International Journal of Forecasting, 23(3), 391–404. https://doi.org/https://doi.org/10.1016/j.ijforecast.2007.05.016
   Web of Science ®Google Scholar
• Head, B. W. (2010). Reconsidering evidence-based policy: Key issues and challenges. Policy and Society, 29(2), 77–94. https://doi.org/https://doi.org/10.1016/j.polsoc.2010.03.001
   Web of Science ®Google Scholar
• Heuts, F., & Mol, A. (2013). What is a good tomato? A case of valuing in practice. Valuation Studies, 1(2), 125–146. https://doi.org/https://doi.org/10.3384/vs.2001-5992.1312125
   Google Scholar
• Kemper, J., & Kolkman, D. (2019). Transparent to whom? No algorithmic accountability without a critical audience. Information, Communication & Society, 22(14), 2081–2096. https://doi.org/https://doi.org/10.1080/1369118X.2018.1477967
   Web of Science ®Google Scholar
• Knorr-Cetina. (1981). The manufacture of knowledge: Towards a constructivist and contextual view of science. Pergamon.
   Google Scholar
• Kolkman, D. (2020). The usefulness of algorithmic models in policy making. Government Information Quaterly. https://doi.org/https://doi.org/10.1016/j.giq.2020.101488
   Google Scholar
• Kolkman, D. A., Campo, P., Balke-Visser, T., & Gilbert, N. (2016). How to build models for government: Criteria driving model acceptance in policymaking. Policy Sciences, 49(4), 489–504. https://doi.org/https://doi.org/10.1007/s11077-016-9250-4
   Web of Science ®Google Scholar
• Kuhn, T. S. (1962). The structure of scientific revolutions. University of Chicago press.
   Google Scholar
• Latour, B. (1987). Science in action: How to follow scientists and engineers through society. Harvard University Press.
   Google Scholar
• Law, J., & Mol, A. (2006). The actor-enacted: Cumbrian Sheep in 2001. In C. Knappett & L. Malafouris (Eds.), Material agency (pp. 57–77). Springer US.
   Google Scholar
• Mazzotti, M. (2017). Algorithmic life. Retrieved February 12, 2020, from https://lareviewofbooks.org/article/algorithmic-life/
   Google Scholar
• Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 68–82. https://doi.org/https://doi.org/10.1177/2053951716679679
   Web of Science ®Google Scholar
• Pasquale, F. (2015). The black box society. Harvard University Press.
   Google Scholar
• Patton, M. Q. (2002). Purposeful sampling. In Qualitative evaluation and research methods (Vol. 3). Sage.
   Google Scholar
• Peng, G., Ritchey, N. A., Casey, K. S., Kearns, E. J., Privette, J. L., Saunders, D., & Ansari, S. (2016). Scientific stewardship in the open data and big data era – roles and responsibilities of stewards and other major product stakeholders. D-Lib Magazine, 22(5-6), 1–14.
   Google Scholar
• Pielke, R. A. (1999). Who decides? Forecasts and responsibilities in the 1997 red river flood. Applied Behavioral Science Review, 7(2), 83–101. https://doi.org/https://doi.org/10.1016/S1068-8595(00)80012-4
   Google Scholar
• Shapin, S. (1995). Cordelia’s love: Credibility and the social studies of science. Perspectives on Science, 3(3), 255–275. http://nrs.harvard.edu/urn-3:HUL.InstRepos:3293019
   Google Scholar
• Stewart Jr., J., Hedge, D. M., & Lester, J. P. (2007). Public policy: An evolutionary approach. Nelson Education.
   Google Scholar
• Strauss, A. L., & Corbin, J. M. (1998). Basics of qualitative research: Techniques and procedures for developing grounded theory. Routledge.
   Google Scholar
• Taylor, L. (2017). What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, 4(2), 205395171773633. https://doi.org/https://doi.org/10.1177/2053951717736335
   Web of Science ®Google Scholar
• Treasury, H. M. (2013). Review of quality assurance of government analytical models: Final report.
   Google Scholar
• van Daalen, C. E., Dresen, L., & Janssen, M. A. (2002). The roles of computer models in the environmental policy life cycle. Environmental Science & Policy, 238, 1–11. https://doi.org/https://doi.org/10.1016/S1462-9011
   Google Scholar
• Van der Sluijs, J. P. (2002). A way out of the credibility crisis of models used in integrated environmental assessment. Futures, 34(2), 133–146. https://doi.org/https://doi.org/10.1016/S0016-3287(01)00051-9
   Web of Science ®Google Scholar
• van Doorn, N. (2017). Platform labor: On the gendered and racialized exploitation of low-income service work in the ‘on-demand’ economy. Information, Communication & Society, 20(6), 898–914. https://doi.org/https://doi.org/10.1080/1369118X.2017.1294194
   Web of Science ®Google Scholar
• Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/https://doi.org/10.1111/j.1540-5915.2008.00192.x
   Web of Science ®Google Scholar
• Venturini, T., Jensen, P., & Latour, B. (2015). Fill in the gap. A new alliance for social and natural sciences. Journal of Artificial Societies and Social Simulation, 18(1), 1–4. https://doi.org/https://doi.org/10.18564/jasss.2729
   Google Scholar
• Walker, W. E., Harremoës, P., Rotmans, J., van der Sluijs, J. P., van Asselt, M. B. A., Janssen, P., & Krayer von Krauss, M. P. (2003). Defining uncertainty: A conceptual basis for uncertainty management in model-based decision support. Integrated Assessment, 4(1), 5–17. https://doi.org/https://doi.org/10.1076/iaij.4.1.5.16466
   Google Scholar