Foresight in Hindsight: An Insight into Ahmed v. Stefaniu and De-Biasing Legal Evaluations of Reasonable Care
Bibliographic record
Abstract
The recent Ontario Court of Appeal decision in Ahmed v. Stefaniu, [2006] O.J. No. 4185 (C.A.) is considered in detail. This case involved a jury verdict of negligence against a psychiatrist who had previously decided that a patient who ultimately murdered his sister ought to be a voluntary patient. Ms. Pengelley argues that hindsight bias came into play when the jury considered whether Dr. Stefaniu had met the required standard of care. Ms. Pengelley's treatise references the seminal 1975 study of psychologist Baruch Fischoff. He underlined the principle that not only did learning of the outcome create hindsight bias, but also that we appear unable to disregard such information. Educating individuals about hindsight bias and asking them to be careful not to be influenced by the information appears to have little effect. Perhaps the best explanation as to why people show hindsight bias, rests in the idea that people try to make post facto sense of the world around them. Possible solutions to these difficulties are found in bifurcated trials. This can involve the jury hearing evidence about the defendant's conduct and first making a decision about whether the requisite standard of care has been met. Then, it can hear evidence about causation and quantum of damages. Alternatively, a second jury could be asked to determine the damages issue. It is argued that Binnie J. opened the door to bifurcation in Whiten v. Pilot Insurance Co., [2002] 1 S.C.R. 595. To date, the case law indicates that Canadian trial courts should exercise bifurcation only in the clearest cases. Ms. Pengelley admits that bifurcation is not necessarily foolproof. Bifurcated trials also are not very practical tools in our overburdened judicial system. Nonetheless, counsel's awareness of potential hindsight bias will allow them to tailor their advocacy to take this key factor into consideration in order to improve their odds of success.
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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 | 0.004 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".