Fairness, accountability and transparency: notes on algorithmic decision-making in criminal justice
Bibliographic record
Abstract
Abstract Over the last few years, legal scholars, policy-makers, activists and others have generated a vast and rapidly expanding literature concerning the ethical ramifications of using artificial intelligence, machine learning, big data and predictive software in criminal justice contexts. These concerns can be clustered under the headings of fairness, accountability and transparency. First, can we trust technology to be fair, especially given that the data on which the technology is based are biased in various ways? Second, whom can we blame if the technology goes wrong, as it inevitably will on occasion? Finally, does it matter if we do not know how an algorithm works or, relatedly, cannot understand how it reached its decision? I argue that, while these are serious concerns, they are not irresolvable. More importantly, the very same concerns of fairness, accountability and transparency apply, with even greater urgency, to existing modes of decision-making in criminal justice. The question, hence, is comparative: can algorithmic modes of decision-making improve upon the status quo in criminal justice? There is unlikely to be a categorical answer to this question, although there are some reasons for cautious optimism.
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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 | 0.051 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.072 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.010 | 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".