IEEE standard review — Ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems
Bibliographic record
Abstract
In September 2009, the IEEE Board of Directors approved the new IEEE tagline - Advancing Technology for Humanity - as recommended by the IEEE Public Visibility Committee. Aligned with the IEEE tagline, IEEE Standards Association takes the initiative to address ethics in engineering design under “Ethically Aligned Design: A Vision for Prioritizing Human Wellbeing with Artificial Intelligence and Autonomous Systems” focusing on Artificial Intelligence and Autonomous Systems (AI/AS). The intention is to cover, as much as possible, the ethical concerns on AI/AS through a rigorous regard to the problem from different perspectives. The ultimate objective of this ongoing initiative is to provide guidelines/procedures/standards to prioritize human wellbeing in the forthcoming evolutions on artificial intelligence and autonomous systems. This article reviews different aspects addressed in Version 1 of this initiative.
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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 | 0.310 | 0.430 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.011 | 0.009 |
| Research integrity | 0.029 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 0.010 |
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".