Knowing the Mind from Brain Data: The Challenge of Prediction and the Fairness of Relying on Objective Data about the Mind
Bibliographic record
Abstract
This article is the second in a series examining the ethical and social implications of inferring mental states from brain data. It considers two main topics. First, it discusses the challenges of extending inferences from present brain activity to mental states and from there to future mental states or behaviors. There is a risk of compounded errors when multiple inferential models are applied sequentially; harmful outcomes for minority groups underrepresented in statistical models could result. In addition, predictions based on brain data may create self-fulfilling prophecies, reinforcing neuroessentialist beliefs that undermine personal agency. Second, the paper discusses how concerns related to epistemic injustice might arise when mental states are inferred from brain data. In particular, it asks if it is just to privilege "objective" brain-based conclusions over individuals' subjective self-reports. While brain-based evidence could empower some people to prove their claims, it may also exacerbate credibility gaps and social biases. The article concludes that careful, context-specific assessments are essential before brain-based inferences are adopted in socially significant decisions.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".