Brain–computer interfaces and personhood: interdisciplinary deliberations on neural technology
Bibliographic record
Abstract
OBJECTIVE: Scientists, engineers, and healthcare professionals are currently developing a variety of new devices under the category of brain-computer interfaces (BCIs). Current and future applications are both medical/assistive (e.g. for communication) and non-medical (e.g. for gaming). This array of possibilities has been met with both enthusiasm and ethical concern in various media, with no clear resolution of these conflicting sentiments. APPROACH: To better understand how BCIs may either harm or help the user, and to investigate whether ethical guidance is required, a meeting entitled 'BCIs and Personhood: A Deliberative Workshop' was held in May 2018. MAIN RESULTS: We argue that the hopes and fears associated with BCIs can be productively understood in terms of personhood, specifically the impact of BCIs on what it means to be a person and to be recognized as such by others. SIGNIFICANCE: Our findings suggest that the development of neural technologies raises important questions about the concept of personhood and its role in society. Accordingly, we propose recommendations for BCI development and governance.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".