Artificial Free Thought: Automated Courts and the Independent Algorithm
Bibliographic record
Abstract
This chapter reflects on a constitutional problem of the algorithmic state: the automation of courts with the help of artificial intelligence (AI) technologies and its implications for judicial independence. Judicial AI systems, designed by or in collaboration with non-state actors, introduce a new form of influence permeated by technological ideologies. At the same time, automation promises court decisions that are less vulnerable to human biases. This chapter reframes this issue that I describe as the ‘independent algorithm’ problem. The chapter begins by reviewing the theory and history of judicial independence. I find that for historical reasons, traditional conceptions of independence are often bound to the separation of powers doctrine and that as a result, the ideological power of non-state actors such as technological companies has been in large measure ignored in constitutional design. I then review current automated courts initiatives and the actors involved behind the scenes. The fourth and final section of the chapter ponders the ‘independent algorithm’ problem. Making use of the chapter’s findings regarding the foundations of judicial independence, I assess whether the principle, as understood and implemented in constitutional language, can speak to the phenomenon of automated courts. My conclusion is that this is not the case, and thus I plant the seeds for an epistemological approach to judicial independence based on the concept of free thought as envisioned by Bertrand Russell. Artificial free thought is a conception of independence that strives to ensure the intellectual independence of automated court ‘judges’ from all sources of bias, including their designers.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".