Corporate Criminal Liability for Algorithmic Price Fixing in Canada
Bibliographic record
Abstract
The use of computerized algorithms is increasingly common in the modern business environment. An algorithm can be defined as ‘‘a set of mathematical instructions or rules that, especially if given to a computer, will help to calculate an answer to a problem.” As noted in this definition, algorithms are particularly powerful tools when combined with computing power. The proliferation of computerized algorithms in business settings has occasionally led to unintended and injurious outcomes. This is perhaps most notable in relation to the algorithmic trading of securities. The 2010 ‘‘Flash Crash” of the United States (U.S.) financial markets, during which key markets lost and then regained over a trillion dollars in value over the span of 36 minutes, was caused, at least in part, by the intentional manipulation of algorithmic trading processes. Another example is that of Knight Capital, a financial services firm, which, in 2012, lost approximately USD 440 million in just 45 minutes due to a faulty algorithm. Unsurprisingly, securities regulators stand at the forefront of regulating algorithms, with U.S. and European (E.U.) agencies both developing policies in this regard. Complying with regulations aimed at algorithms will be a novel challenge for the financial trading industry.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".