Bibliographic record
Abstract
In modern society, algorithms play an important role in social and cultural realms, in political and economic spheres. In spite of algorithmic pervasiveness in many areas and wide diffusion in digital life, algorithmic opacity is still poorly understood compared to other ethical issues (e.g., fairness, accountability, and transparency). In this essay, we try to elucidate the relation between algorithmic opacity and moral certainty from the individualistic standpoint and through the virtue ethic perspective. For doing so, we follow hermeneutic tradition and rely on interpretation of recent authors and impactful papers. We summarize our argument as follows: if the algorithm is understood as the combination of rules and numbers we create for simplifying our lives and sharing with others, then our present activities and future actions as imagined, realized or missed, ascertain if algorithmic opacity become a moral issue or problem for us and others. Among the implications, we emphasize that sometime dormant and hard to anticipate, algorithmic opacity becomes an apparent during executions, deployments and prolonged uses of algorithmic systems. Moreover, our lived experience and disharmony between our unrealized expectations and unanticipated algorithmic behavior may lead to moral issues and problems for us and others. Overall, algorithmic opacity may constantly evade the formalization efforts (e.g., outlining as guidelines, principles) or quantification exercises (e.g., assigning numerical values to symbols or signs), both of which are essentially social practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".