scholarly journals Can Artificial Intelligence (“AI”) Replace Human Arbitrators? Technological Concerns and Legal Implications

2021 ◽  
Author(s):  
Gizem Halis Kasap
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Commercial Arbitration ◽  
Legal Scholarship ◽  
Legal Studies ◽  
Legal Decision Making ◽  
Legal Implications ◽  
Wide Range ◽  
Heated Debate ◽  
Compare And Contrast

Artificial intelligence (“AI”) has advanced to the point that machines can compare and contrast historical cases in order to predict the outcome of a dispute at hand. AI studies that predict the outcome of litigation have stirred heated debate about the possible arrival of AI judges. Few scientific and legal studies have investigated the prediction of legal decision-making in arbitration despite the fact of AI predicting case outcomes and the emerging talk of AI judges. Inspired by these debates and to fill this gap in legal scholarship, the article poses the question of whether AI will be able to replace arbitrators, enquires into AI’s ability to predict outcomes of future cases in the context of international commercial arbitration, and scouts the potential implications if AI does, in fact, replace arbitrators. Although this article focuses on the international arbitration perspective, it looks at examples and studies from a wide range of legal fields, and its findings apply adjudicatory decision-making more generally.

Human-Compatible Artificial Intelligence

2021 ◽  
pp. 3-23
Author(s):  
Stuart Russell
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Standard Model ◽  
Real World ◽  
Technical Aspects ◽  
Alan Turing ◽  
Wide Range ◽  
The Standard Model ◽  
Formal Class

Following the analysis given by Alan Turing in 1951, one must expect that AI capabilities will eventually exceed those of humans across a wide range of real-world-decision making scenarios. Should this be a cause for concern, as Turing, Hawking, and others have suggested? And, if so, what can we do about it? While some in the mainstream AI community dismiss the issue, I will argue that the problem is real: we have to work out how to design AI systems that are far more powerful than ourselves while ensuring that they never have power over us. I believe the technical aspects of this problem are solvable. Whereas the standard model of AI proposes to build machines that optimize known, exogenously specified objectives, a preferable approach would be to build machines that are of provable benefit to humans. I introduce assistance games as a formal class of problems whose solution, under certain assumptions, has the desired property.

Emotion: A Very Short Introduction

2019 ◽  
Author(s):  
Dylan Evans
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Human Nature ◽  
Middle Ages ◽  
Emotional Impact ◽  
Neural Basis ◽  
Short Introduction ◽  
New Developments ◽  
Biological Basis ◽  
Wide Range

Was love invented by European poets in the Middle Ages or is it part of human nature? Will winning the lottery really make you happy? Is it possible to build robots that have feelings? Emotion: A Very Short Introduction explores the latest thinking about emotions, drawing on a wide range of scientific research, from anthropology and psychology to neuroscience and artificial intelligence. It discusses the evolution of emotions and their biological basis, the science of happiness, and the role that emotions play in memory and decision-making. This new edition has been updated to incorporate new developments in our understanding of emotions, including the neural basis of empathy and the emotional impact of films.

Law as an Autonomous Discipline

2005 ◽  
Author(s):  
Brian H. Bix
Keyword(s):  
Decision Making ◽  
Legal Reasoning ◽  
Legal Decision ◽  
Legal Doctrine ◽  
Legal Scholarship ◽  
Legal Decision Making

This article considers claims about the ‘autonomy of law’. These are that that legal reasoning is different from other forms of reasoning; that legal decision-making is different from other forms of decision-making; that legal reasoning and decision-making are sufficient to themselves, that they neither need help from other approaches nor would they be significantly improved by such help; and that legal scholarship should be about distinctively legal topics (often referred to as ‘legal doctrine’) and is not or should not be about other topics.

scholarly journals Machine Learning for the Improvement of Deep Renovation Building Projects Using As-Built BIM Models

2021 ◽  
Vol 13 (12) ◽  
pp. 6576
Author(s):  
Sofía Mulero-Palencia ◽  
Sonia Álvarez-Díaz ◽  
Manuel Andrés-Chicote
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
New Technologies ◽  
Information Modeling ◽  
Design Stage ◽  
Building Projects ◽  
Efficiency Measures ◽  
Wide Range ◽  
Diagnosis Tool

In recent years, new technologies, such as Artificial Intelligence, are emerging to improve decision making based on learning. Their use applied to the Architectural, Engineering and Construction (AEC) sector, together with the increased use of Building Information Modeling (BIM) methodology in all phases of a building’s life cycle, is opening up a wide range of opportunities in the sector. At the same time, the need to reduce CO2 emissions in cities is focusing on the energy renovation of existing buildings, thus tackling one of the main causes of these emissions. This paper shows the potentials, constraints and viable solutions of the use of Machine Learning/Artificial Intelligence approaches at the design stage of deep renovation building projects using As-Built BIM models as input to improve the decision-making process towards the uptake of energy efficiency measures. First, existing databases on buildings pathologies have been studied. Second, a Machine Learning based algorithm has been designed as a prototype diagnosis tool. It determines the critical areas to be solved through deep renovation projects by analysing BIM data according to the Industry Foundation Classes (IFC4) standard and proposing the most convenient renovation alternative (based on a catalogue of Energy Conservation Measures). Finally, the proposed diagnosis tool has been applied to a reference test building for different locations. The comparison shows how significant differences appear in the results depending on the situation of the building and the regulatory requirements to which it must be subjected.

scholarly journals E-Era of Jurisdiction:Empowering Traditional Courts Using Various Artificial Intelligence Tools

2018 ◽  
Vol 7 (2) ◽  
pp. 57-61
Author(s):  
Balam Singh Dafauti
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Cloud Computing ◽  
Judicial System ◽  
Judicial Process ◽  
Wide Range ◽  
Artificial Intelligence Technology ◽  
Tools And Techniques ◽  
Analytical Tools ◽  
Indian Scenario

In Indian scenario, we are still in the transformation phase from manual to electronic data processing. We are in balanced combination of simple, moral, responsive and transparent governance and IT tools and techniques. However a lot of scope is still there to do more and to imply IT in various governmental departments and domains. In the same sequence we can use artificial intelligence along with cloud computing to improve Indian Judicial system. Or we can say that the concept of e-courts can be enhanced by implying AI tools and techniques. The judiciary is in the early stages of a transformation in which AI (Artificial Intelligence) technology will help to make the judicial process faster, cheaper, and more predictable without compromising the integrity of judges’ discretionary reasoning. In this paper I have proposed a solution where judicial system with AI contributes to a process that encompasses such a wide range of knowledge, judgment, and experience. It have two more practical goals: producing tools to support judicial activities, including programs for intelligent document assembly, case retrieval, and support for discretionary decision-making; and developing new analytical tools for understanding and modeling the judicial process.

scholarly journals DARLENE – Improving situational awareness of European law enforcement agents through a combination of augmented reality and artificial intelligence solutions

2021 ◽  
Vol 1 ◽  
pp. 87
Author(s):  
Konstantinos C. Apostolakis ◽  
Nikolaos Dimitriou ◽  
George Margetis ◽  
Stavroula Ntoa ◽  
Dimitrios Tzovaras ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Computer Vision ◽  
Law Enforcement ◽  
Augmented Reality ◽  
Pose Estimation ◽  
Situational Awareness ◽  
Estimation Method ◽  
Violence Detection ◽  
Wide Range

Background: Augmented reality (AR) and artificial intelligence (AI) are highly disruptive technologies that have revolutionised practices in a wide range of domains. Their potential has not gone unnoticed in the security sector with several law enforcement agencies (LEAs) employing AI applications in their daily operations for forensics and surveillance. In this paper, we present the DARLENE ecosystem, which aims to bridge existing gaps in applying AR and AI technologies for rapid tactical decision-making in situ with minimal error margin, thus enhancing LEAs’ efficiency and Situational Awareness (SA). Methods: DARLENE incorporates novel AI techniques for computer vision tasks such as activity recognition and pose estimation, while also building an AR framework for visualization of the inferenced results via dynamic content adaptation according to each individual officer’s stress level and current context. The concept has been validated with end-users through co-creation workshops, while the decision-making mechanism for enhancing LEAs’ SA has been assessed with experts. Regarding computer vision components, preliminary tests of the instance segmentation method for humans’ and objects’ detection have been conducted on a subset of videos from the RWF-2000 dataset for violence detection, which have also been used to test a human pose estimation method that has so far exhibited impressive results and will constitute the basis of further developments in DARLENE. Results: Evaluation results highlight that target users are positive towards the adoption of the proposed solution in field operations, and that the SA decision-making mechanism produces highly acceptable outcomes. Evaluation of the computer vision components yielded promising results and identified opportunities for improvement. Conclusions: This work provides the context of the DARLENE ecosystem and presents the DARLENE architecture, analyses its individual technologies, and demonstrates preliminary results, which are positive both in terms of technological achievements and user acceptance of the proposed solution.

Stereotypes and legal decision making

2014 ◽  
Author(s):  
John G. Conway ◽  
Scott R. Tindale
Keyword(s):  
Decision Making ◽  
Legal Decision ◽  
Legal Decision Making
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