Technological Prejudice: Demonstrating the Ontological Challenge of Building a Critical Theory of Artificial Intelligence
Bibliographic record
Abstract
This paper contributes to a theory of artificial legal intelligence (ALI) that harmonizes concerns for artificial intelligence (AI) bias and prejudice with 1) the critical perspective, and (2) Jacques Ellul’s critique of the “technological phenomenon”. Necessary to this contribution is an argument for the importance of ontology in understanding the multidimensionality of ALI, and critical theory’s ability to deal with this multidimensionality. First, the paper introduces critical theory and some of its tenets. My focus then is critical legal studies (CLS) and their contentious relationship with the ontological issue of instrumentality. I emphasize that one way a theory of ALI can engage with this critical theme is through an ontological classification of AI. I propose two classifications: AI as a tool and AI as an ideological phenomenon. Each classification is attributive of a certain autonomy to AI and telling about a potentiality for domination a critical theory of ALI should recognize, deconstruct, and challenge. Ellul’s argument that the technological phenomenon is “autonomous” informs this part of my argument. I then discuss the concept of “prejudice” and find that, considering the ontological classifications, prejudice is visible in more than one form. Although the “algorithmic bias” approach is adequate for AI as a tool, it does not account effectively for another form of prejudice rooted in technology. I call it technological prejudice.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".