Artificial Free Thought: Automated Courts and the Independent Algorithm
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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