Law, Language, And Authority: The Algorithmic Turn
Bibliographic record
Abstract
Law is formed by language and law utilizes language. Law is also like language in that it consists of social rules that aid in the structuring of society. From the time that we first put language into writing, we have been invested in the technologizing of language. There is a clear trajectory of our interest in having machines do things with language that we would otherwise do ourselves. This dissertation investigates how law’s relationship with language changes with the use of algorithmically driven technologies, and correspondingly, the consequences for the changing nature of authority since the use of language in law is closely entwined with the use of language in exercising authority.\n\nDrawing on J. L. Austin’s speech-act theory as framework, which scrutinizes language as a form of action and effects rather than as a medium for transmitting information, this dissertation is divided into three pillars that grapple with how algorithms do things with words in the context of law. The first pillar offers an analysis of generative AI and the implications for authorship. The second pillar moves from algorithms and authored words to an examination of algorithms and drafted words, specifically through an analysis of the nature of the emerging “algorithmic contract,” in which an algorithm fills in for human expertise in the contracting process. The third pillar of this dissertation investigates the consequences of executing algorithmic contracts, paying particular attention to the accelerating issue of technology-facilitated gender-based violence in the ride-hailing industry.\n\nTaken together these three pillars have implications for understanding law’s authority as we adopt increasingly sophisticated technologies in society. This final chapter on authority taps into cyberfeminism to help elucidate the changing nature of authority as we delegate authority, often unintentionally, to algorithms.\n\nThe findings drawn from this investigation offer solutions to some of the legal conundrums posed by algorithmically driven technologies that do things with language. These findings also have import for the relationship between law and language and for better understanding the nature of law in the Algorithmic Turn.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".