Three sociologists publish generative AI framework on SocArXiv

Three sociologists have published a theoretical framework arguing that what sets generative AI apart from earlier computational tools is its interactivity. In a SocArXiv preprint posted October 25 by Laura K. Nelson, Daniel Karell, and Thomas Davidson, the authors write that these systems 'are trained on social exchanges, refined by human feedback, and used through dialogue and delegation, so that interaction runs throughout their development and use.'

The paper has been circulating among researchers we follow in the week since it went up.

Their framework describes how 'generative AI expands users, functions as a proxy for society, and expresses power,' and traces that process across individual, organizational and institutional levels. The paper closes by naming three dynamics the authors say will challenge sociology in the near future: 'the transformation of scientific practice, the reorganization of social interaction, and the acceleration of algorithmic governance.'

No empirical measurements, cases, or results appear in the abstract; the paper is pitched as a conceptual framework.