Far Beyond Baker: Heuristics and the Inadequacy of the Reasonable Apprehension of Bias Analysis
Bibliographic record
Abstract
When we consider bias in an adjudicative setting, we think about cases such as Baker v. Canada where the interviewing officer’s emphasis on the applicant’s number of children, economic status, and mental health is glaring. It is easy to become accustomed to thinking about bias in clear examples such as prejudice against people of a disadvantaged group. However, bias can also be subtly present in the subconscious mind, even when a person appears to be acting objectively. The development of cognitive psychology research has revealed that the mind relies on heuristics, or mental shortcuts, to make quick decisions. Heuristics research divides the mind into two systems of operation: The first operates automatically, while the second operates methodically. Heuristics are a function of the former and are consequently difficult to clearly observe and readily identify. Subconscious reliance on heuristics when approaching tasks that demand objectivity can result in decisions that are unintentionally guided by bias, rather than evidence. A reasonable apprehension of bias (RAB) claim is advanced when a party suspects bias on the part of an adjudicator. The question is whether an informed reasonable person would reasonably perceive bias on the part of the adjudicator. The onus is not to prove bias, but merely an appearance of bias. The test is an appeal to the maxim that justice must not only be done but should be seen to be done. The analysis is designed to protect not only the parties but also the public perception of the legal system. In this sense, the RAB test is overinclusive. Conversely, consideration of the RAB test, in light of cognitive biases and heuristic reasoning, reveals that the test is also underinclusive. The driving force behind the RAB analysis appears to be that justice must be seen to be done. Neither the informed reasonable person’s knowledge of the law nor the community can help discover veiled or subtle cognitive biases. We are left with a conundrum: The original premise that justice must be done may be violated covertly without triggering the prevailing test for a RAB, leaving an unintentional blind spot. In order to address the shortcomings of the RAB analysis, I propose the implementation of preventative measures to decrease adjudicators’ vulnerability to cognitive biases. These measures can be established by increasing adjudicators’ understanding of heuristics and counteracting potential intuitive predispositions. Development of remedial measures should consider scarce judicial and adjudicative resources.
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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 | 0.112 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.008 | 0.112 |
| Scholarly communication | 0.016 | 0.030 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.010 | 0.025 |
| 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".