Critiquing technology ethics in prescription and practice: Rendering technical in computer science curriculum design and disability-focused artificial intelligence
Bibliographic record
Abstract
How to engage scientists and technologists with the sociopolitical and environmental consequences of their work remains an open concern for the Science and Technology Studies (STS) community.In recent years, computer science has made a greater push for teaching ethical and social issues in light of societal "techlash".Past work in STS has described how computing education as teaching undergraduates to "render technical" (Breslin 2018): to take complex, real-world problems, strip away the sociohistorical context, and solve only the technical problem.And given that past work in the history of computing has identified curriculum reports of the Association for Computing Machinery (ACM) and Institute of Electrical and Electronics Engineers Computer Society (IEEE-CS) as significant for shaping the field, we performed a discourse analysis of the most recent computing curricula standards from the ACM and IEEE-CS.We found that although the curriculum report states ethics education is important and should be spread throughout the curriculum, the curriculum standards themselves discursively compartmentalize ethics and then render it into the technical problems of reducing "error" from professionals and enacting a (hegemonic) benevolent designer-user relationship.We illustrate how a scientific field can give an impression of commitment to social and professional education without engaging with the underlying issues, and compare conceptual frameworks for understanding hegemonic ideology in computing.25c0000.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".