AI and social theory

Abstract

In this paper, we sketch a programme for AI-driven social theory. We begin by defining what we mean by artificial intelligence (AI) in this context. We then lay out our specification for how AI-based models can draw on the growing availability of digital data to help test the validity of different social theories based on their predictive power. In doing so, we use the work of Randall Collins and his state breakdown model to exemplify that, already today, AI-based models can help synthesise knowledge from a variety of sources, reason about the world, and apply what is known across a wide range of problems in a systematic way. However, we also find that AI-driven social theory remains subject to a range of practical, technical, and epistemological limitations. Most critically, existing AI-systems lack three essential capabilities needed to advance social theory in ways that are cumulative, holistic, open-ended, and purposeful. These are (1) semanticisation, i.e., the ability to develop and operationalize verbal concepts to represent machine-manipulable knowledge; (2) transferability, i.e., the ability to transfer what has been learned in one context to another; and (3) generativity, i.e., the ability to independently create and improve on concepts and models. We argue that if the gaps identified here are addressed by further research, there is no reason why, in the future, the most advanced programme in social theory should not be led by AI-driven cumulative advances.

Artificial Intelligence and Democracy: A Conceptual Framework

“…Going further, AI is increasingly used within the social sciences as a method of discovery. The associated shifts in scientific workflow, questions asked, and approaches to theorizing are a fertile area for future reflection (Grimmer et al, 2021; Mökander & Schroeder, 2022).…”

Artificial intelligence-related anomies and predictive policing: normative (dis)orders in liberal democracies

“…(EU-Commission 2019) With this definition, the phenomena to be described and investigated in the following can be adequately comprehended, especially in the field of predictive policing. By understanding AI in this way as a set of systems that actually make decisions themselves, i.e., machine learning, they inevitably become part of an intertwining socio-technical system (Mökander and Schroeder 2022) that also implies human tendencies, vulnerabilities and attitudes.…”

Gender Segregation in the Borderlands of E-Science

“…New tools and applications of the rising, male-dominated data sciences (Boston Consulting Group Gamma Study, 2019), such as AI and machine learning (ML), have recently been assembled in e-science platforms without due attention to how gender figures within these. STS scholars who study e-science have started to shift their attention to data science (Ribes, 2019;Paine and Lee, 2020;Beaulieu and Leonelli, 2021;Mökander and Schroeder, 2021). With this shift of attention gendered asymmetries which are sunk into the infrastructure might become further invisibilised.…”