Bibliographic record
Abstract
Abstract This chapter describes algorithmic decision-making (ADM) systems. ADM systems are tools that leverage an algorithmic process to arrive at some form of decision such as a scoring, ranking, classification, or association that may then drive further system action and behavior. Such systems could be said to exhibit artificial intelligence (AI) insofar as they contribute to decision-making tasks that might normally be undertaken by humans. However, it is important to underscore that ADM systems must be understood as composites of nonhuman actors woven together with human actors such as designers, data-creators, maintainers, and operators into complex sociotechnical assemblages. If the end goal is accountability, then transparency must serve to help locate the various positions of human agency and responsibility in these large and complex sociotechnical assemblages. Ultimately, it is people who must be held accountable for the behavior of algorithmic systems. The chapter then highlights what is needed to realistically implement algorithmic transparency in terms of what is disclosed and how and to whom transparency information is disclosed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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; 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".