Ethical and Societal Impacts of Generative AI in Higher Computing Education: An ACM Task Force Working Group to Develop a Landscape Analysis - Perspectives from the Global Souths and Guidelines for CS1/CS2/CS3
Bibliographic record
Abstract
Generative AI has a wide range of impacts on how we access and use information, particularly as educational settings and perspectives differ greatly across different locations. These impacts extend to society and include impacts on intellectual and creative works and the potential infringement of authorship. Differences in institutional GenAI policies (and in funding) may create unequal access to AI tools, the potential disparity in student knowledge of AI tools, responsible uses of AI tools, ethical questions about AI tools, and uneven student knowledge of the benefits and limitations of AI tools. Generative AI introduces questions concerning academic integrity, bias, and data provenance. The training data's source, reliability, veracity, and trustworthiness may be in doubt, creating broader societal concerns about the output of the Generative AI models. This working group will conduct a landscape analysis on Global South ethical questions related to the use of Generative AI tools in higher education contexts, identifying promising principles, challenges, and ways to navigate the implementation of Generative AI in ethical and principled ways.
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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 | 0.119 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.028 | 0.016 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".