300090 Trust and Mistrust in Science, Experts and Expert Systems (S) (SoSe 2024)

Skepticism about science and expertise is evident today in multiple arenas and clearly can no longer be considered a passing phenomenon. From climate change to vaccines; from mammograms to masks to Coronavirus testing; from opposition to rules regulating acceptable levels of carcinogens to AI algorithms purporting to yield better decisions than human experts; the challenges to the authority of experts come from both sides of the political spectrum and take multiple forms.
Most discussions of the challenges to expertise start from the question of mistrust. They ask why do people mistrust science and experts given their obvious and well-documented successes and their contribution to immense improvements in collective well-being? Why would people refuse to be vaccinated for COVID-19? This way of posing the question typically leads to the conclusion that the people who mistrust experts are irrational, uninformed or duped.
This, however, may not be the most productive way to pose the question. For the social scientist, mistrust is not the puzzle, trust is. Is it really all that surprising that people would mistrust decisions taken in locales far removed from their daily lives, that are supported by forms of knowledge and technical arrangements that can be relatively opaque and difficult to grasp? Moreover, these decisions typically impact some individuals and groups adversely, while benefitting others (essential workers and small business owners are harmed by lockdowns; people who can work from home benefit from them). Finally, every now and then, but predictably so, it becomes obvious in hindsight that the wrong decisions were taken. Under these circumstances it is far more puzzling, counter-intuitive and difficult to understand why, how, and under what conditions people do trust science and experts.
If we want to understand mistrust, and ultimately arrive at a more balanced arrangement of the relations between experts and laypeople, we need to begin by asking what is trust? How is it typically organized and secured? What sustains trust in experts and scientists? Only after we have some grasp of the mechanisms by which trust is cultivated, can we hope to have an explanation of the contemporary atmosphere of mistrust. Only in this way can we hope to discuss these issues in a forthright manner without the discussion deteriorating into name-calling: “climate denier,” “anti-vaxxer,” etc.

May 28 What is expertise?

Andrew Abbott, 1988. The System of Professions. The University of Chicago Press: 1-85.
Steven Epstein, “The Construction of Lay Expertise: AIDS Activism and the Forging of Credibility in the Reform of Clinical Trials,” Science, Technology and Human Values, 20, 4 (Autumn 1995), 408-437.
Hubert Dreyfus and Stuart E. Dreyfus, “Peripheral Vision: Expertise in Real World Contexts,” Organization Studies, 26, 5 (2005), 779-792.
Harry Collins and Robert Evans, Rethinking Expertise. (Chicago: University of Chicago Press, 2007), 1-44.
Gil Eyal, “For a Sociology of Expertise: The Social Origins of the Autism Epidemic,” AJS Vol. 118, No. 4 (January 2013), pp. 863-907.
Eyal, 2019. The Crisis of Expertise, Polity: 1-42.
Ruthanne Huising, 2023. “Professional Authority,” in Eyal and Medvetz, The Oxford Handbook of Expertise and Democratic Politics. Oxford University Press: 453-469.

June 4 What is trust?

Niklas Luhmann, 2017 [1973]. “Trust,” in Trust and Power. Polity Press: pp.3-114. [You may read in the original German.]
Anthony Giddens, 1990. The Consequences of Modernity. Stanford, CA: Stanford University Press, 17-36, 79-100, 112-124.
Pierre Bourdieu, 2000. Pascalian Meditations. Polity: pp.188-202.
Guido Mollering, Trust: Reason, Routine, Reflexivity. Amsterdam: Elsevier, 2006: 1-125.
Eyal, 2019. The crisis of expertise, 43-63.
Paper progress report is due June 11

June 18 How to study trust in experts?

Brownlie and Howson, “Leaps of Faith and MMR: an Empirical Study of Trust,” Sociology 39, 2 (2005).
Brownlie, Greene and Howson, Researching Trust and Healt 2008.h. Routledge: 17-32, 72-112, 133-151.
Jennifer A. Reich, Calling the Shots: Why Parents Reject Vaccines. NYU Press, 2018. 1-23, 32-87, 97-139, 145-158.
Larry Au and Gil Eyal, 2022. “Whose Advice is Credible: Claiming Lay Expertise in a Covid-19 Online Community,” Qualitative Sociology 45: 31-61. https://doi.org/10.1007/s11133-021-09492-1
Gil Eyal, 2022. “Mistrust in Numbers: Regulatory Science, Trans-science and the Crisis of Expertise,” Spontaneous Generations 10(1): 36-46.
Gil Eyal, Larry Au and Cristian Capotescu, “Trust methods: Accounting for Who, What, When and How to Trust.” Unpublished manuscript.

June 25 The problem of Artificial Intelligence

Harry Collins, 2018. Artifictional Intelligence. Polity: 1-56.
Brian Christian, 2020. The Alignment Problem. W. W. Norton and Company: pages TBA.
Aviad Raz, Netta Avnoon, Gil Eyal, Bert Hienrichs and Iael Inbar, “Prediction and Explainability in AI: Striking a New Balance?” Big Data & Society, January-March: 1-5. DOI: 10.1177/20539517241235871
Hannah Pullen-Blasnik, Amy Weissenbach, and Gil Eyal, 2024. “Is Your Accuser Me, or Is It the Software?” Ambiguity and Contested Expertise in Probabilistic DNA Profiling.” Social Studies of Science 54 (1). https://doi.org/10.1177/03063127231186646
Min Kung Lee, “Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management,” Big Data & Society January–June 2018: 1–16.
Pierce RL, Van Biesen W, Van Cauwenberge D, Decruyenaere J and Sterckx S (2022), Explainability in medicine in an era of AI-based clinical decision support systems. Front. Genet. 13:903600. doi: 10.3389/fgene.2022.903600
Marion Fourcade and Fleur Johns, “Loops, ladders and links: the recursivity of social
and machine learning,” Theory and Society (2020) 49:803–832
https://doi.org/10.1007/s11186-020-09409-x
Benjamin Shestakofsky and Shreeharsh Kelkar, “Making platforms work: relationship labor and the management of publics,” Theory and Society (2020) 49:863–896
https://doi.org/10.1007/s11186-020-09407-z
Jeannette Wing, 2021. “Trustworthy AI.” Communications of the ACM 64, 10: pp 64–71. https://doi.org/10.1145/3448248