What is "ethical AI"? Leading or participating on an ethical team and/or working in statistics, data science, and artificial intelligence
Bibliographic record
Abstract
Artificial Intelligence (AI) arises from computing and statistics, and as such, can be developed and deployed ethically when the ethical practice standards of each of these fields are followed. The Toronto Declaration was formulated in 2018 specifically to ensure that machine learning and AI could be held accountable for respecting, and promoting, universal human rights. The Code of Ethics and Professional Conduct of the Association of Computing Machinery (ACM, 2018) and the Ethical Guidelines for Statistical Practice of the American Statistical Association (ASA, 2022) describe the ethical practice standards for any person at any level of training or job title who utilizes computing (ACM) or statistical practices (ASA). These three reference documents can together define "what is ethical AI". All development, deployment, and use of computing is covered by the ACM Code; the ASA defines statistical practice to "include activities such as: designing the collection of, summarizing, processing, analyzing, interpreting, or presenting, data; as well as model or algorithm development and deployment.” Just as the Toronto Declaration describes universal human rights protections, the ACM and ASA ethical practice standards apply to professionals, individuals with diverse background or jobs that include computing and statistical practices at any point, and employers, clients, organizations, and institutions that employ or utilize the outputs from computing and statistical practices worldwide. The ACM Code of Ethics has four Principles, including one specifically for Leaders with seven elements. The ASA Ethical Guidelines include eight principles and an Appendix; one Guideline Principle (G. Responsibilities of Leaders, Supervisors, and Mentors in Statistical Practice) with its five elements and the Appendix (Responsibilities of organizations/institutions) with its 12 elements are specifically intended to support workplace engagement with, and support of, ethical statistical practices, plus, the specific roles and responsibilities of those in leadership positions. These ethical practice standards can support both individual practitioners', and leaders', meeting their obligations for ethical AI worldwide.
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How this classification was reachedexpand
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 | 0.013 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".