ETHICS-2025 Session A4 -Workshop: IEEE Neuroethics Framework World Café: ETHICS-2025 Special Session, Saturday, June 7 2025, 10:30 AM - 12:00 PM CDT.
Bibliographic record
Abstract
The IEEE Neuroethics Framework is an international, multi-year, volunteer-led initiative by IEEE BRAIN designed to provide ethical guidance for engineers, researchers, applied scientists, practitioners, and neurotechnology companies. It brings together a range of stakeholders, including engineers, scientists, clinicians, ethicists, legal experts, social scientists, and those with lived experience to develop a comprehensive ethical framework across nine different applications: Medical, Wellness, Education, Work, Employment, Military, Sports, Entertainment, and Marketing. This workshop provides an avenue to solicit new perspectives for three applications: Workplace, Entertainment, and Sports. It uses a “World Café” style method that the IEEE Neuroethics Framework team has employed previously to generate novel insights across a variety of stakeholders. Participants will leave with an appreciation for the complexities of current and emerging neurotech across a range of applications, with a particular focus on neurotech in the workplace, entertainment, and sports. Their feedback will directly impact the production and revision of a series of white papers that, together, make up the IEEE Neuroethics Framework.
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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.149 | 0.062 |
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".