The Long Road Toward Tracking the Trackers and De-biasing: A Consensus on Shaking the Black Box and Freeing From Bias
Bibliographic record
Abstract
Automated decision making is both promising and threatening. Processing the biggest data possible may lead to societal advances but also violate human rights. There is, then, an acute need to protect individuals without impeding major benefits. Non-human agents may be biased; and they may not lend themselves to easy explanations. Instead of focusing on interpreting models, there seems to be a shift toward a concept of risk assessments. Opaque systems are aimed at predicting, or forecasting, future situations. This challenges human values and ethical principles. Even though incorporating ethics in machines is an old subject of legal discussion, consensus has not yet been reached; for theories and values may be controversial. This paper examines whether there could be an agreement on fundamental principles. A commonly understood basis could allow for fair and proportionate mechanisms to address crucial aspects of partiality and opacity in automated decision making. It could trigger a shift toward a concept of ‘tracking the trackers’ and a discussion on a ‘right to an unbiased decision maker’.
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.
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 | 0.007 | 0.005 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".