Toward a Sociology of Artificial Intelligence: A Call for Research on Inequalities and Structural Change
Bibliographic record
Abstract
This article outlines a research agenda for a sociology of artificial intelligence (AI). The authors review two areas in which sociological theories and methods have made significant contributions to the study of inequalities and AI: (1) the politics of algorithms, data, and code and (2) the social shaping of AI in practice. The authors contrast sociological approaches that emphasize intersectional inequalities and social structure with other disciplines’ approaches to the social dimensions of AI, which often have a thin understanding of the social and emphasize individual-level interventions. This scoping article invites sociologists to use the discipline’s theoretical and methodological tools to analyze when and how inequalities are made more durable by AI systems. Sociologists have an ability to identify how inequalities are embedded in all aspects of society and to point toward avenues for structural social change. Therefore, sociologists should play a leading role in the imagining and shaping of AI futures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.096 |
| Scholarly communication | 0.015 | 0.045 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".