Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Artificial intelligence (AI) and algorithmic systems have been criticized for perpetuating bias, unjust discrimination, and contributing to inequality. Artificial intelligence researchers have remained largely oblivious to existing scholarship on social inequality, but a growing number of sociologists are now addressing the social transformations brought about by AI. Where bias is typically presented as an undesirable characteristic that can be removed from AI systems, engaging with social inequality scholarship leads us to consider how these technologies reproduce existing hierarchies and the positive visions we can work towards. I argue that sociologists can help assert agency over new technologies through three kinds of actions: (1) critique and the politics of refusal; (2) fighting inequality through technology; and (3) governance of algorithms. As we become increasingly dependent on AI and automated systems, the dangers of further entrenching or amplifying social inequalities have been well documented, particularly with the growing adoption of these systems by government agencies. However, public policy also presents some opportunities to restructure social dynamics in a positive direction, as long as we can articulate what we are trying to achieve, and are aware of the risks and limitations of utilizing these new technologies to address social problems.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it