Why STS is not the solution to “tech ethics”
by Sebastian Benthall
“Tech ethics” are in (1) (2) (3) and a popular refrain at FAT* this year was that sensitivity to social and political context is the solution to the problems of unethical technology. How do we bring this sensitivity to technical design? Using the techniques of Science and Technology Studies (STS), argue variously Dobbe and Ames, as well as Selbst et al. (2019). Value Sensitive Design (VSD) (Friedman and Bainbridge, 2004) is one typical STS-branded technique for bringing this political awareness into the design process. In general, there is broad agreement that computer scientists should be working with social scientists when developing socially impactful technologies.
In this blog post, I argue that STS is not the solution to “tech ethics” that it tries to be.
Encouraging computer scientists to collaborate with social science domain experts is a great idea. My paper with Bruce Haynes (1) (2) (3) is an example of this kind of work. In it, we drew from sociology of race to inform a technical design that addressed the unfairness of racial categories. Significantly, in my view, we did not use STS in our work. Because the social injustices we were addressing were due to broad reaching social structures and politically constructed categories, we used sociology to elucidate what was at stake and what sorts of interventions would be a good idea.
It is important to recognize that there are many different social sciences dealing with “social and political context”, and that STS, despite its interdisciplinarity, is only one of them. This is easily missed in an interdisciplinary venue in which STS is active, because STS is somewhat activist in asserting its own importance in these venues. STS frequently positions itself as a reminder to blindered technologists that there is a social world out there. “Let me tell you about what you’re missing!” That’s it’s shtick. Because of this positioning, STS scholars frequently get a seat at the table with scientists and technologists. It’s a powerful position, in sense.
What STS scholars tend to ignore is how and when other forms of social scientists involve themselves in the process of technical design. For example, at FAT* this year there were two full tracks of Economic Models. Economic Models. Economics is a well-established social scientific discipline that has tools for understanding how a particular mechanism can have unintended effects when put into a social context. In economics, this is called “mechanism design”. It addresses what Selbst et al. might call the “Ripple Effect Trap”–the fact that a system in context may have effects that are different from the intention of designers. I’ve argued before that wiser economics are something we need to better address technology ethics, especially if we are talking about technology deployed by industry, which is most of it! But despite deep and systematic social scientific analysis of secondary and equilibrium effects at the conference, these peer-reviewed works are not acknowledged by STS interventionists. Why is that?
As usual, quantitative social scientists are completely ignored by STS-inspired critiques of technologists and their ethics. That is too bad, because at the scale at which these technologies are operating (mainly, we are discussing civic- or web-scale automated decision making systems that are inherently about large numbers of people), fuzzier debates about “values” and contextualized impact would surely benefit from quantitative operationalization.
The problem is that STS is, at its heart, a humanistic discipline, a subfield of anthropology. If and when STS does not deny the utility or truth or value of mathematization or quantification entirely, as a field of research it is methodologically skeptical about such things. In the self-conception of STS, this methodological relativism is part of its ethnographic rigor. This ethnographic relativism is more or less entirely incompatible with formal reasoning, which aspires to universal internal validity. At a moralistic level, it is this aspiration of universal internal validity that is so bedeviling to the STS scholar: the mathematics are inherently distinct from an awareness of the social context, because social context can only be understood in its ethnographic particularity.
This is a false dichotomy. There are other social sciences that address social and political context that do not have the same restrictive assumptions of STS. Some of these are quantitative, but not all of them are. There are qualitative sociologists and political scientists with great insights into social context that are not disciplinarily allergic to the standard practices of engineering. In many ways, these kinds of social sciences are far more compatible with the process of designing technology than STS! For example, the sociology we draw on in our “Racial categories in machine learning” paper is variously: Gramscian racial hegemony theory, structuralist sociology, Bourdieusian theories of social capital, and so on. Significantly, these theories are not based exclusively on ethnographic method. They are based on disciplines that happily mix historical and qualitative scholarship with quantitative research. The object of study is the social world, and part of the purpose of the research is to develop politically useful abstractions from it that generalize and can be measured. This is the form of social sciences that is compatible with quantitative policy evaluation, the sort of thing you would want to use if, for example, understanding the impact of an affirmative action policy.
Given the widely acknowledge truism that public sector technology design often encodes and enacts real policy changes (a point made in Deirdre Mulligan’s keynote), it would make sense to understand the effects of these technologies using the methodologies of policy impact evaluation. That would involve enlisting the kinds of social scientific expertise relevant to understand society at large!
But that is absolutely not what STS has to offer. STS is, at best, offering a humanistic evaluation of the social processes of technology design. The ontology of STS is flat, and its epistemology and ethics are immediate: the design decision comes down to a calculus of “values” of different “stakeholders”. Ironically, this is a picture of social context that often seems to neglect the political and economic context of that context. It is not an escape from empty abstraction. Rather, it insists on moving from clear abstractions to more nebulous ones, “values” like “fairness”, maintaining that if the conversation never ends and the design never gets formalized, ethics has been accomplished.
This has proven, again and again, to be a rhetorically effective position for research scholarship. It is quite popular among “ethics” researchers that are backed by corporate technology companies. That is quite possibly because the form of “ethics” that STS offers, for all of its calls for political sensitivity, is devoid of political substance. An apples-to-apples comparison of “values”, without considering the social origins of those values and the way those values are grounded in political interests that are not merely about “what we think is important in life”, but real contests over resource allocation. The observation by Ames et al. (2011) that people’s values with respect to technology varies with socio-economic class is terribly relevant, Bourdieusian lesson in how the standpoint of “values sensitivity” may, when taken seriously, run up against the hard realities of political agonism. I don’t believe STS researchers are truly naive about these points; however, in their rhetoric of design intervention, conducted in labs but isolated from the real conditions of technology firms, there is an idealism that can only survive under the self-imposed severity of STS’s own methodological restrictions.
Independent scholars can take up this position and publish daring pieces, winning the moral high ground. But that is not a serious position to take in an industrial setting, or when pursuing generalizable knowledge about the downstream impact of a design on a complex social system. Those empirical questions require different tools, albeit far more unwieldy ones. Complex survey instruments, skilled data analysis, and substantive social theory are needed to arrive at solid conclusions about the ethical impact of technology.
Ames, M. G., Go, J., Kaye, J. J., & Spasojevic, M. (2011, March). Understanding technology choices and values through social class. In Proceedings of the ACM 2011 conference on Computer supported cooperative work (pp. 55-64). ACM.
Friedman, B., & Bainbridge, W. S. (2004). Value sensitive design.
Selbst, A. D., Friedler, S., Venkatasubramanian, S., & Vertesi, J. (2018, August). Fairness and Abstraction in Sociotechnical Systems. In ACM Conference on Fairness, Accountability, and Transparency (FAT*).