The Application of Artificial Intelligence Technology in the US Civil Court System
Notice bibliographique
Résumé
Introduction: in the era of the active introduction of digital technologies, more and more processes are being automated and smart machines are taking over the work of people. Even at the end of the 20th century, automatic spell-checking and search engines were perceived by many as “highly intelligent” information technologies. Currently, such processes have become completely trivial for most people and have given way to more advanced technologies. The intelligent face recognition systems installed in public places and airports allow you to verify a person’s identity, as well as assist in the capture of criminals. The smart assistants in mobile devices, for example, Google Maps, provide additional information about the destination (working hours, the name of the organization). However, there is more and more debate about the introduction of artificial intelligence technologies in the judicial process. Many experts in the field of information and communication technologies, as well as practicing lawyers, argue that thanks to the accumulated experience and judicial practice, it is possible to predict and make court decisions based on certain algorithms for certain categories of cases. This practice already exists in the system of alternative settlement of civil disputes. The first such decision was made by a robot mediator back in 2019 in the High Court of England and Wales. To resolve the dispute, the Smartsettle ONE system developed by the Canadian company iCan Systems was used. The use of artificial intelligence technology allowed resolving the dispute between the parties and coming to an agreement in less than an hour. The legislator approaches the issues of the introduction of artificial intelligence technology in the system of state courts more carefully. However, court cases do not always require a comprehensive individual approach to decision-making and many cases can be processed automatically, at least, partially. In this regard, it seems appropriate to explore in the paper the main opportunities and risks of using artificial intelligence through the example of the civil justice system of the United States of America. The purpose of the study is achieved by answering several questions: how can artificial intelligence be useful for courts? What mechanisms of the justice system need to be improved for the effective operation of artificial intelligence systems? What forms of artificial intelligence exist in the US civil court system? How can courts and judges work with artificial intelligence under the standards of a fair procedure for considering civil disputes? The methodology is based on a theoretical approach to the study of the most commonly used artificial intelligence technologies in the US civil justice system, as well as a number of national laws and other regulations. Based on the analysis of the theoretical data obtained, in the paper, the author analyzes the current trends and mechanisms for resolving civil disputes using artificial intelligence systems and also highlights some related problems. The results of the research can be used in determining the key goals and objectives of a procedural nature, improving the functioning of judicial and non-judicial organizations, law enforcement, research activities, as well as in teaching activities, in particular, during lectures and seminars on courses of private international law and civil procedure. Conclusions: increasing the level of awareness of participants in civil law disputes about current trends and tools for the administration of justice contributes to the development of the institution of civil proceedings, as well as contributes to increasing transparency and increasing the degree of trust of citizens in the judicial system as a whole.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».