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.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".