CURRENT STATE AND PERSPECTIVES USE OF ARTIFICIAL INTELLIGENCE IN THE JUDICIAL EXPERTISE

2021 ◽  
Author(s):  
A.A. Bessonov
Keyword(s):  
Artificial Intelligence ◽  
Current State ◽  
Judicial Expertise

Artificial Intelligence and Inequality in the Middle East

2020 ◽  
pp. 624-649 ◽  
Author(s):  
Nagla Rizk
Keyword(s):  
Artificial Intelligence ◽  
Human Resources ◽  
Young Population ◽  
Mena Region ◽  
Poor Countries ◽  
Current State ◽  
Resource Poor ◽  
Youth Entrepreneurship ◽  
The Arab Spring ◽  
The Impact

This chapter looks at the challenges, opportunities, and tensions facing the equitable development of artificial intelligence (AI) in the MENA region in the aftermath of the Arab Spring. While diverse in their natural and human resource endowments, countries of the region share a commonality in the predominance of a youthful population amid complex political and economic contexts. Rampant unemployment—especially among a growing young population—together with informality, gender, and digital inequalities, will likely shape the impact of AI technologies, especially in the region’s labor-abundant resource-poor countries. The chapter then analyzes issues related to data, legislative environment, infrastructure, and human resources as key inputs to AI technologies which in their current state may exacerbate existing inequalities. Ultimately, the promise for AI technologies for inclusion and helping mitigate inequalities lies in harnessing grounds-up youth entrepreneurship and innovation initiatives driven by data and AI, with a few hopeful signs coming from national policies.

Effects of Deep Learning Technologies on Employment in the Field of Digital Communication Systems

2021 ◽  
Vol 12 (4) ◽  
pp. 35-42
Author(s):  
Thomas Alan Woolman ◽  
Philip Lee
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Communication Systems ◽  
Digital Communication ◽  
The United States ◽  
Learning Technologies ◽  
Technological Unemployment ◽  
Current State ◽  
Challenges And Opportunities ◽  
Digital Communication Systems

There are significant challenges and opportunities facing the economies of the United States in the coming decades of the 21st century that are being driven by elements of technological unemployment. Deep learning systems, an advanced form of machine learning that is often referred to as artificial intelligence, is presently reshaping many aspects of traditional digital communication technology employment, primarily network system administration and network security system design and maintenance. This paper provides an overview of the current state-of-the-art developments associated with deep learning and artificial intelligence and the ongoing revolutions that this technology is having not only on the field of digital communication systems but also related technology fields. This paper will also explore issues and concerns related to past technological unemployment challenges, as well as opportunities that may be present as a result of these ongoing technological upheavals.

STATE OF DOCTRINAL RESEARCH ON THE LEGAL REGULATION OF ARTIFICIAL INTELLIGENCE IN RUSSIA BASED ON THE WORKS OF RUSSIAN LAWYERS

2021 ◽  
pp. 11-22
Author(s):  
Galina Andreeva ◽  
Keyword(s):  
Artificial Intelligence ◽  
Legal Regulation ◽  
Scientific Development ◽  
Current State

This review summarizes the statements of Russian scientists about the current state of scientific development of issues of legal regulation of AI, the complexities of the problems facing scientists and the assessment of the proposed ways to solve them in the most important aspects of legal regulation of AI.

scholarly journals Ethical issues on artificial intelligence in radiology: how is it reported in research articles? The current state and future directions

2021 ◽  
Vol 90 (2) ◽  
pp. e513
Author(s):  
Tomasz Piotrowski ◽  
Joanna Kazmierska ◽  
Mirosława Mocydlarz-Adamcewicz ◽  
Adam Ryczkowski
Keyword(s):  
Artificial Intelligence ◽  
Ethical Issues ◽  
Real Life ◽  
Exclusion Criteria ◽  
Future Directions ◽  
Current State ◽  
Ethical Status ◽  
The Status ◽  
Clinical Questions ◽  
Almost All

Background. This paper evaluates the status of reporting information related to the usage and ethical issues of artificial intelligence (AI) procedures in clinical trial (CT) papers focussed on radiology issues as well as other (non-trial) original radiology articles (OA). Material and Methods. The evaluation was performed by three independent observers who were, respectively physicist, physician and computer scientist. The analysis was performed for two groups of publications, i.e., for CT and OA. Each group included 30 papers published from 2018 to 2020, published before guidelines proposed by Liu et al. (Nat Med. 2020; 26:1364-1374). The set of items used to catalogue and to verify the ethical status of the AI reporting was developed using the above-mentioned guidelines. Results. Most of the reviewed studies, clearly stated their use of AI methods and more importantly, almost all tried to address relevant clinical questions. Although in most of the studies, patient inclusion and exclusion criteria were presented, the widespread lack of rigorous descriptions of the study design apart from a detailed explanation of the AI approach itself is noticeable. Few of the chosen studies provided information about anonymization of data and the process of secure data sharing. Only a few studies explore the patterns of incorrect predictions by the proposed AI tools and their possible reasons. Conclusion. Results of review support idea of implementation of uniform guidelines for designing and reporting studies with use of AI tools. Such guidelines help to design robust, transparent and reproducible tools for use in real life.

scholarly journals The Concept of Culpability in Criminal Law and AI Systems

2020 ◽  
Vol 13 (3) ◽  
pp. 256
Author(s):  
Roman Dremliuga ◽  
Natalia Prisekina
Keyword(s):  
Artificial Intelligence ◽  
Criminal Law ◽  
Criminal Liability ◽  
Legal Framework ◽  
Social Consequences ◽  
Artificial Intelligence System ◽  
Traditional Concept ◽  
Potential Harm ◽  
Intelligence System ◽  
Current State

This article focuses on the problems of the application of AI as a tool of crime from the perspective of the norms and principles of Criminal law. The article discusses the question of how the legal framework in the area of culpability determination could be applied to offenses committed with the use of AI. The article presents an analysis of the current state in the sphere of criminal law for both intentional and negligent offenses as well as a comparative analysis of these two forms of culpability. Part of the work is devoted to culpability in intentional crimes. Results of analysis in the paper demonstrate that the law-enforcer and the legislator should reconsider the approach to determining culpability in the case of the application of artificial intelligence systems for committing intentional crimes. As an artificial intelligence system, in some sense, has its own designed cognition and will, courts could not rely on the traditional concept of culpability in intentional crimes, where the intent is clearly determined in accordance with the actions of the criminal. Criminal negligence is reviewed in the article from the perspective of a developer’s criminal liability. The developer is considered as a person who may influence on and anticipate harm caused by AI system that he/she created. If product developers are free from any form of criminal liability for harm caused by their products, it would lead to highly negative social consequences. The situation when a person developing AI system has to take into consideration all potential harm caused by the product also has negative social consequences. The authors conclude that the balance between these two extremums should be found. The authors conclude that the current legal framework does not conform to the goal of a culpability determination for the crime where AI is a tool.

Predictive Models in Urology

2013 ◽  
Vol 80 (1) ◽  
pp. 42-45 ◽  
Author(s):  
Andrea Cestari
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Predictive Modeling ◽  
Predictive Models ◽  
Machine Intelligence ◽  
Health Records ◽  
Knowledge Based ◽  
Current State ◽  
Computer Based ◽  
Future Direction

Predictive modeling is emerging as an important knowledge-based technology in healthcare. The interest in the use of predictive modeling reflects advances on different fronts such as the availability of health information from increasingly complex databases and electronic health records, a better understanding of causal or statistical predictors of health, disease processes and multifactorial models of ill-health and developments in nonlinear computer models using artificial intelligence or neural networks. These new computer-based forms of modeling are increasingly able to establish technical credibility in clinical contexts. The current state of knowledge is still quite young in understanding the likely future direction of how this so-called ‘machine intelligence’ will evolve and therefore how current relatively sophisticated predictive models will evolve in response to improvements in technology, which is advancing along a wide front. Predictive models in urology are gaining progressive popularity not only for academic and scientific purposes but also into the clinical practice with the introduction of several nomograms dealing with the main fields of onco-urology.

scholarly journals Applications of artificial intelligence (AI) in diagnostic radiology: a technography study

2020 ◽  
Author(s):  
Mohammad Hosein Rezazade Mehrizi ◽  
Peter van Ooijen ◽  
Milou Homan
Keyword(s):  
Artificial Intelligence ◽  
Body Part ◽  
Diagnostic Radiology ◽  
Radiology Workflow ◽  
Stage Of Development ◽  
Current State ◽  
Key Points ◽  
Technological Developments ◽  
Anatomic Region ◽  
Systematic Framework

Abstract Objectives Why is there a major gap between the promises of AI and its applications in the domain of diagnostic radiology? To answer this question, we systematically review and critically analyze the AI applications in the radiology domain. Methods We systematically analyzed these applications based on their focal modality and anatomic region as well as their stage of development, technical infrastructure, and approval. Results We identified 269 AI applications in the diagnostic radiology domain, offered by 99 companies. We show that AI applications are primarily narrow in terms of tasks, modality, and anatomic region. A majority of the available AI functionalities focus on supporting the “perception” and “reasoning” in the radiology workflow. Conclusions Thereby, we contribute by (1) offering a systematic framework for analyzing and mapping the technological developments in the diagnostic radiology domain, (2) providing empirical evidence regarding the landscape of AI applications, and (3) offering insights into the current state of AI applications. Accordingly, we discuss the potential impacts of AI applications on the radiology work and we highlight future possibilities for developing these applications. Key Points • Many AI applications are introduced to the radiology domain and their number and diversity grow very fast. • Most of the AI applications are narrow in terms of modality, body part, and pathology. • A lot of applications focus on supporting “perception” and “reasoning” tasks.

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