Algorithmic Harms and Algorithmic Wrongs
Bibliographic record
Abstract
New artificial intelligence (AI) systems grounded in machine learning are being integrated into our lives at a rapid rate, but not without consequence: scholars across domains have increasingly pointed out issues related to privacy, transparency, bias, discrimination, exploitation, and exclusion associated with algorithmic systems in both public and private sector contexts. Concerns surrounding the adverse impacts of these technologies have spurred discussion on the topics of algorithmic harm. However, the overwhelming majority of articles on said harms offer no definition as to what constitutes ‘harm’ in these contexts. This paper aims to address this omission by introducing one criterion for a suitable account of algorithmic harm. More specifically, we follow Joel Feinberg in understanding harms as distinct from wrongs, where only the latter necessarily carry a normative dimension. This distinction highlights issues in the current scholarship surrounding the conflation of algorithmic harms and wrongs. In response to these issues, we put forth two requirements for upholding the harms/wrongs distinction when analyzing the increasingly far-reaching impacts of these technologies and suggest how this distinction can be useful in design, engineering, and policymaking.
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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 | 0.037 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.085 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".