Algorithmic discrimination causes less moral outrage than human discrimination.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
hypothesis in the context of gender discrimination in hiring practices across diverse participant groups (online samples, a quasi-representative sample, and a sample of tech workers). We find that people are less morally outraged by algorithmic (vs. human) discrimination and are less likely to hold the organization responsible. The algorithmic outrage deficit is driven by the reduced attribution of prejudicial motivation to algorithms. Just as algorithms dampen outrage, they also dampen praise-companies enjoy less of a reputational boost when their algorithms (vs. employees) reduce gender inequality. Our studies also reveal a downstream consequence of algorithmic outrage deficit-people are less likely to find the company legally liable when the discrimination was caused by an algorithm (vs. a human). We discuss the theoretical and practical implications of these results, including the potential weakening of collective action to address systemic discrimination. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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.
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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it