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 This chapter canvasses academic literature on artificial intelligence (AI) and judging. While the idea of ‘robot judges’ has been discussed with derision and fear, there is great promise for AI to improve the judiciary, both in terms of expediting process and in terms of the improving the substantive decisions of human judges. But the use of AI by judges is highly controversial. While much of the academic literature on AI and judging is relatively recent, it is already vast. The chapter is divided into four main topics. It begins by defining what is meant in this chapter by AI, a notoriously fuzzy concept. The field is broad, and the definition is constantly changing. The author primarily focuses on tools of prediction, such as supervised machine learning. Second, the chapter discusses how scholars of judicial behaviour have used machine learning tools to make predictions of how judges will behave. It then explores how courts around the world have already begun to use machine learning predictions in their decisions. Finally, literature outlining concerns and risks if AI tools were to be used more widely by judges is covered. Topics such as transparency, explanations, trust, bias, and error are explored.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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