rusjelRussian Journal of Economics and LawRussian Journal of Economics and Law2782-2923"TCE "Taglimat"" Ltd.10.21202/2782-2923.2024.1.217-245rusjel-2522Research ArticleПЕРЕВОДНЫЕ СТАТЬИTRANSLATED ARTICLESЧего следует ожидать от искусственного интеллекта?What Should we Reasonably Expect from Artificial Intelligence?https://orcid.org/0000-0002-3593-2831ПарентониЛ.ParentoniL.

Леонардо Парентони, профессор права

г. Белу-Оризонти

Leonardo Parentoni, Tenured Law Professor

Belo Horizonte

Федеральный университет Минас-Жерайс (ФУМЖ)БразилияFederal University of Minas Gerais – UFMGBrazil202419032024181217245Copyright © Парентони Л., 20242024Парентони Л.Parentoni L.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.rusjel.ru/jour/article/view/2522

Цель: изучение несоответствия между ожиданиями от систем искусственного интеллекта (ИИ) и их текущими возможностями. В настоящее время искусственный интеллект является широко распространенной и передовой технологией и используется в различных отраслях, таких как сельское хозяйство, промышленность, торговля, образование, профессиональные услуги, «умные города» и киберзащита. Однако несоответствие между ожиданиями от искусственного интеллекта и его текущими возможностями приводит к двум нежелательным последствиям. Во-первых, от ИИ ожидают результатов, выходящих за рамки его нынешней стадии развития, что приводит к нереалистичным требованиям. Во-вторых, возникает неудовлетворенность существующими возможностями ИИ, хотя во многих контекстах они достаточны.Методы: для решения проблемы несоответствия в статье используется аналитический подход. Анализируются различные рыночные приложения ИИ, раскрывается их разнообразие. Показано, что ИИ не является однородной, единой концепцией, но включает в себя широкий спектр отраслевых приложений, каждое из которых служит своим целям, обладает присущими ему рисками и соответствует определенным уровням точности.Результаты: основной вывод статьи заключается в том, что несоответствие между ожиданиями и реальными возможностями ИИ возникает из-за ошибочной предпосылки, что системы ИИ должны всегда достигать точности, значительно превосходящей человеческие стандарты, независимо от контекста. Рассматривая различные рыночные приложения, автор выступает за то, чтобы оценивать потенциал ИИ и приемлемые уровни точности и прозрачности в зависимости от контекста. Результаты показывают, что для каждого приложения ИИ должны быть приняты свои целевые показатели точности и прозрачности, подбираемые в каждом конкретном случае. Следовательно, системы ИИ применимы в различных контекстах, даже если их точность или прозрачность ниже возможностей человека.Научная новизна: статья опровергает широко распространенное заблуждение о том, что ИИ должен работать со сверхчеловеческой точностью и прозрачностью во всех сценариях. Раскрывая разнообразие приложений ИИ и их задач, автор подчеркивает, что ожидания и оценки должны быть адаптированы к конкретному контексту использования.Практическая значимость: статья представляет ценность для заинтересованных сторон в области ИИ, включая регулирующие органы, разработчиков и потребителей. Пересмотр ожиданий на основе контекста способствует принятию обоснованных решений и повышению ответственности при разработке и внедрении ИИ. Работа помогает повысить общий уровень использования и принятия технологий ИИ за счет более реалистичного понимания возможностей и ограничений ИИ в различных контекстах. Автор предлагает более широкий взгляд на проблему, призывая к созданию надежной нормативно-правовой базы и ответственному внедрению систем ИИ, что будет способствовать улучшению применения ИИ в различных секторах. Кроме того, более реальные требования позволят обеспечить понимание путей развития и регулирования ИИ. 

Objective: the objective of this article is to address the misalignment between the expectations of Artificial Intelligence (or just AI) systems and what they can currently deliver. Despite being a pervasive and cutting-edge technology present in various sectors, such as agriculture, industry, commerce, education, professional services, smart cities, and cyber defense, there exists a discrepancy between the results some people anticipate from AI and its current capabilities. This misalignment leads to two undesirable outcomes: Firstly, some individuals expect AI to achieve results beyond its current developmental stage, resulting in unrealistic demands. Secondly, there is dissatisfaction with AI's existing capabilities, even though they may be sufficient in many contexts.Methods: the article employs an analytical approach to tackle the misalignment issue, analyzing various market applications of AI and unveils their diversity, demonstrating that AI is not a homogeneous, singular concept. Instead, it encompasses a wide range of sector-specific applications, each serving distinct purposes, possessing inherent risks, and aiming for specific accuracy levels.Results: the primary finding presented in this article is that the misalignment between expectations and actual AI capabilities arises from the mistaken premise that AI systems should consistently achieve accuracy rates far surpassing human standards, regardless of the context. By delving into different market applications, the author advocates for evaluating AI's potential and accepted levels of accuracy and transparency in a context-dependent manner. The results highlight that each AI application should have different accuracy and transparency targets, tailored on a case-by-case basis. Consequently, AI systems can still be valuable and welcomed in various contexts, even if they offer accuracy or transparency rates lower or much lower than human standards.Scientific novelty: the scientific novelty of this article lies in challenging the widely held misconception that AI should always operate with superhuman accuracy and transparency in all scenarios. By unraveling the diversity of AI applications and their purposes, the author introduces a fresh perspective, emphasizing that expectations and evaluations should be contextualized and adapted to the specific use case of AI.Practical significance: the practical significance of this article lies in providing valuable guidance to stakeholders within the AI field, including regulators, developers, and customers. The article's realignment of expectations based on context fosters informed decision-making and promotes responsible AI development and implementation. It seeks to enhance the overall utilization and acceptance of AI technologies by promoting a realistic understanding of AI's capabilities and limitations in different contexts. By offering more comprehensive guidance, the article aims to support the establishment of robust regulatory frameworks and promote the responsible deployment of AI systems, contributing to the improvement of AI applications in diverse sectors. The author's call for fine-tuned expectations aims to prevent dissatisfaction arising from unrealistic demands and provide solid guidance for AI development and regulation.

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In CHI Conference on Human Factors in Computing Systems Proceedings (CHI 2019), May 4–9, 2019, Glasgow, Scotland.* Принадлежит нежелательной организации в РФ / Belongs to an undesirable organization in the Russian Federation.* Принадлежит нежелательной организации в РФ / Belongs to an undesirable organization in the Russian Federation.** Признана нежелательной организацией в РФ с 06.12.2023 / Recognized as an undesirable organization in the Russian Federation** Признана нежелательной организацией в РФ с 06.12.2023 / Recognized as an undesirable organization in the Russian Federationas of 06.12.2023.as of 06.12.2023.*** Признана нежелательной организацией в РФ с 16.02.2026 / Recognized as an undesirable organization in the Russian Federation*** Признана нежелательной организацией в РФ с 16.02.2026 / Recognized as an undesirable organization in the Russian Federationas of 16.02.2026.as of 16.02.2026.

The authors declare that there are no conflicts of interest present.