The biases of experts: An empirical analysis of expert witness challenges
Bibliographic record
Abstract
Biased expert witnesses pose a distinct challenge to the legal system. In the criminal sphere, they have contributed to several wrongful convictions, and in civil cases, they can protract disputes and reduce faith in the legal system. This has inspired a great deal of legal-psychological research studying expert biases and how to mitigate them. In response to the problem of biased experts, courts have historically employed procedural mechanisms to manage partiality, but have generally refrained from using exclusionary rules. Canada diverged from this position in 2015, developing an exclusionary rule in White Burgess Langille Inman v Abbott and Haliburton Co. In this article, we assembled a database of 229 Canadian bias cases pre- and post-White Burgess to evaluate the impact that this case had on the jurisprudence. The data suggests that White Burgess increased the frequency of challenges related to expert biases, however, did not noticeably affect the proportion of experts that were excluded. This suggests that the exclusionary rule introduced in White Burgess did not significantly impact the practical operation of expert evidence law, as it pertains to bias. We conclude by recommending that one way for courts to better address the problem of biased experts is to recognize the issue of contextual bias.
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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 | 0.033 | 0.232 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".