"Not my Priority:" Ethics and the Boundaries of Computer Science Identities in Undergraduate CS Education
Bibliographic record
Abstract
Researchers in the CSCW community have long problematized the separation of social and ethical considerations from design work. Despite increasing attention to tech ethics and ethics education, however, computer scientists' sense of ethical responsibility remains of concern. This paper offers insights on how this boundary between tech and ethics is maintained and reinforced for students as they develop their identities as computer scientists. Drawing on interviews with eight undergraduate computer science (CS) students at McGill University, we explore the role that ethics play in the legitimate peripheral participation of students inside and outside their formal education. We found that while individual opinions on the importance of ethics varied, students agreed that ethics are not valued or rewarded in their education, extracurriculars, or future work prospects. We describe how placing ethics outside the boundary of computing acts as a form of occupational closure, excluding both important multidisciplinary work and marginalized bodies. We argue that in order to promote ethical practice in the design of CSCW systems, we must make it in the interest of future designers to learn socially grounded ethics. This requires that designers, researchers, and future employers actively reshape the boundaries of computing by asserting social and ethical considerations as values of computing and design.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.032 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".