Algorithmic risk governance: Big data analytics, race and information activism in criminal justice debates
Bibliographic record
Abstract
Meanings of risk in criminal justice assessment continue to evolve, making it critical to understand how particular compositions of risk are mediated, resisted and re-configured by experts and practitioners. Criminal justice organizations are working with computer scientists, software engineers and private companies that are skilled in big data analytics to produce new ways of thinking about and managing risk. Little is known, however, about how criminal justice systems, social justice organizations and individuals are shaping, challenging and redefining conventional actuarial risk episteme(s) through the use of big data technologies. The use of such analytics is shifting organizational risk practices, challenging social science methods of assessing risk, producing new knowledge about risk and consequently new forms of algorithmic governance. This article explores how big data reconfigure risk by producing a new form of algorithmic risk—a form of risk which is posited as different from the social science (psychologically) informed risk techniques already in use in many justice sectors. It also shows that new experts are entering the risk game, including technologists who make data public and accessible to a range of stakeholders. Finally, it demonstrates that big data analytics can be used to produce forms of usable knowledge that constitute types of ‘information activism’. This form of activism produces alternative risk narratives, which are focused on ‘criminogenic structures’ or ‘criminogenic policy’.
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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 | 0.028 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.067 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".