Judging by Numbers: How Will Judicial Analytics Impact the Justice System and Its Stakeholders?
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Notice bibliographique
Résumé
In 2019, the French government passed an unprecedented law that banned the public from analyzing information in reported court decisions to draw insights about the judicial behaviour of individual judges. The penalty for breaking this law is steep: violators face up to five years in prison. In our view, a French-style ban is not normatively defensible in Canada given our protection of freedom of expression and our strong open courts principle. The public should be able to analyze information that is in the public domain. We do believe, however, that Canada – like France – faces important questions about how to respond to the fast-growing field of judicial analytics. Although studying judges is not new, judicial analytics tools allow for much faster and more powerful analysis of large amounts of information. Judicial analytics tools for public use already exist but, for reasons explained in the article, such tools are likely to become even more powerful and readily accessible in the near-to-medium future, resulting in unprecedented public insight into judges and the work of judging. We term this phenomenon “mainstreamed judicial analytics.” It is this future world of mainstreamed judicial analytics that is the focus of our article. What happens in a world where technology allows us to instantaneously draw up a detailed profile of a judge’s past behaviour with a click of a smartphone button? What happens when we have a plethora of “stats” on how judges react to particular types of litigants, lawyers, legal arguments or even individual words? What happens when we can pull up reports on how a judge’s behaviour may be impacted by the day of the week, time of day or even the weather? Motivated by these and related questions, this article provides an analysis of the future of judicial analytics, its likely impacts, and potential responses to the rise of this technology in Canada. We conclude that the key potential benefit of mainstreamed judicial analytics is significantly increased transparency into the work of judging. Such transparency could provide an opportunity for the public to better critique and more effectively operate within the justice system. Also, judges could use information produced by judicial analytics tools to reflect on and improve upon their practices, where needed. Meaningful transparency, however, is not a guaranteed output. There will remain some practical complications to producing high-quality information even with “mainstreamed” tools. We also identify potential risks resulting from increased surveillance of judges, including the potential for unwanted strategic behaviour and negative impacts on health and well-being. Finally, we note that lawyers and judges will need to become familiar with this technology in order to competently perform their jobs.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle