Future-proofing integrity in the age of artificial intelligence and neurotechnology: prioritizing human rights, dignity, and equity
Bibliographic record
Abstract
This article I argue for the prioritisation of human rights when developing and implementing misconduct policies. Existing approaches may be perpetuate inequities, particularly for individuals from marginalised groups. A human-rights-by-design approach, which centres human rights in policy development, revision, and implementation, ensuring that every individual is treated with dignity and respect. Recommendations for implementing a human-rights approach to misconduct investigations and case management are offered, covering areas such as procedural fairness, privacy, equity, and the right to education. Additional topics covered are the need to limit surveillance technologies, and the need to recognize that not all use of artificial intelligence tools automatically constitutes misconduct. I disentangle the differences between equity and equality and explain how both are important when considering ethics and integrity. A central argument of this paper is that a human-rights-by-design approach to integrity does not diminish standards but rather strengthens educational systems by cultivating ethical awareness and respect for personhood. I conclude with a call to action with a seven-point plan for institutions to adopt a human-rights-based approach to ethics and integrity. In the age of artificial intelligence and neurotechnology, insisting on human rights and dignity when we investigate and address misconduct allegations is an ethical imperative that has never been more important.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.189 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.014 | 0.106 |
| Scholarly communication | 0.032 | 0.042 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.020 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".