ARTIFICIAL INTELLIGENCE : PROBLEMS AND PROSPECTS OF USING IN THE PRACTICE OF INTERNATIONAL ARBITRATION

2021 ◽  
pp. 123-130
Author(s):  
D.V. Krasikov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Data Processing ◽  
Research Work ◽  
Decision Making Process ◽  
International Arbitration ◽  
Work Done

The review considers the positions of a number of foreign authors on the problems and prospects of using artificial intelligence in the practice of international arbitration. Common to the respective authors’ views is the recognition of the possibility of using AI for data processing within the research work done by the parties while elaborating their positions, as well as for using by arbitrators as a supplementary tool within the decision-making process.

Regret and Disappointment: A Conceptualization of their Role in Ethical Decision-making

2007 ◽  
Vol 32 (4) ◽  
pp. 75-86 ◽  
Author(s):  
Priya Rajeev ◽  
Sanghamitra Bhattacharyya
Keyword(s):  
Decision Making ◽  
Negative Affect ◽  
Ethical Decision ◽  
Ethical Decision Making ◽  
Research Work ◽  
Decision Making Process ◽  
Individual Choice ◽  
Ethical Decisions ◽  
Managerial Decisions ◽  
Work Done

Workplaces provide settings for the manifestation of an assortment of emotions that impact managerial decisions, ethical or otherwise, in a variety of ways. Most of the research work in this domain has concentrated on identifying and analysing the influence of positive affect on decision-making, with little work done on negative affect and its implications. To address this gap, the paper seeks to study the role of negative affect in ethical decision-making by managers. All decisions have outcomes. Post-decision affect may be negative when a decision appears to be wrong in retrospect, and ⁄ or when the outcome of a decision is not what was expected. How does negative affect experienced by an individual as a consequence of a decision impact his⁄her potential ethical decision-making process? In order to develop a model that illustrates how negative affect might impact the components of an individual's ethical decision-making process, this paper makes use of two negative emotions: Regret Dissappointment. Although regret and disappointment have a lot in common, they differ in ways that are relevant to decision-making. Unlike other emotions, regret is unique in its relation to decisionmaking and responsibility. Individuals regret an outcome when they could have taken a different decision and prevented that outcome. Being an outcome of individual choice and hence personal agency, its behavioural consequences comprise an active attempt to undo the unpleasant effects of the decision that went wrong. Disappointment on the contrary is experienced when the negative outcome is the result of a random procedure rather than choice. The behavioural consequences of disappointment might include complaining and talking about the event to others, feelings of powerlessness and a tendency to do nothing and get away from the situation. The paper discusses the possible behavioural consequences of the two emotions in terms of ethical decision-making. As numerous ethical decision-making models have succeeded in integrating personspecific, issue contingent, and organizational contributors to ethical decisions, the need now is to probe further into specific causalities. Understanding affect induced by work is important to gain further insights into the person-specific variables that impact ethical decisions. This paper is an attempt in that direction.

scholarly journals Expert System for Diagnosis of Uterine Myomas using the Certainty Factor Method

2021 ◽  
Vol 1 (4) ◽  
pp. 103-108
Author(s):  
Syahrizal Dwi Putra ◽  
M Bahrul Ulum ◽  
Diah Aryani
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert System ◽  
Computer System ◽  
Decision Making Process ◽  
Certainty Factor ◽  
Uterine Myomas ◽  
Factor Method ◽  
Two Factors

An expert system which is part of artificial intelligence is a computer system that is able to imitate the reasoning of an expert with certain expertise. An expert system in the form of software can replace the role of an expert (human) in the decision-making process based on the symptoms given to a certain level of certainty. This study raises the problem that many women experience, namely not understanding that they have uterine myomas. Many women do not understand and are not aware that there are already symptoms that are felt and these symptoms are symptoms of the presence of uterine myomas in their bodies. Therefore, it is necessary for women to be able to diagnose independently so that they can take treatment as quickly as possible. In this study, the expert will first provide the expert CF values. Then the user / respondent gives an assessment of his condition with the CF User values. In the end, the values obtained from these two factors will be processed using the certainty factor formula. Users must provide answers to all questions given by the system in accordance with their current conditions. After all the conditions asked are answered, the system will display the results to identify that the user is suffering from uterine myoma disease or not. The Expert System with the certainty factor method was tested with a patient who entered the symptoms experienced and got the percentage of confidence in uterine myomas/fibroids of 98.70%. These results indicate that an expert system with the certainty factor method can be used to assist in diagnosing uterine myomas as early as possible.

Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence

2021 ◽  
Author(s):  
Ekaterina Jussupow ◽  
Kai Spohrer ◽  
Armin Heinzl ◽  
Joshua Gawlitza
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Medical Errors ◽  
Decision Makers ◽  
Medical Decision ◽  
Decision Making Process ◽  
Self Monitoring ◽  
Rule Based ◽  
Human Actors

Systems based on artificial intelligence (AI) increasingly support physicians in diagnostic decisions, but they are not without errors and biases. Failure to detect those may result in wrong diagnoses and medical errors. Compared with rule-based systems, however, these systems are less transparent and their errors less predictable. Thus, it is difficult, yet critical, for physicians to carefully evaluate AI advice. This study uncovers the cognitive challenges that medical decision makers face when they receive potentially incorrect advice from AI-based diagnosis systems and must decide whether to follow or reject it. In experiments with 68 novice and 12 experienced physicians, novice physicians with and without clinical experience as well as experienced radiologists made more inaccurate diagnosis decisions when provided with incorrect AI advice than without advice at all. We elicit five decision-making patterns and show that wrong diagnostic decisions often result from shortcomings in utilizing metacognitions related to decision makers’ own reasoning (self-monitoring) and metacognitions related to the AI-based system (system monitoring). As a result, physicians fall for decisions based on beliefs rather than actual data or engage in unsuitably superficial evaluation of the AI advice. Our study has implications for the training of physicians and spotlights the crucial role of human actors in compensating for AI errors.

Organizational Data Mining (ODM)

2011 ◽  
pp. 1-8
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

Artificial Intelligence, Human Intelligence, and Orientism Management Framework

2021 ◽  
pp. 1-30
Author(s):  
Luisa Dall'Acqua
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Makers ◽  
Human Intelligence ◽  
Decision Making Process ◽  
Knowledge Process ◽  
Management Framework ◽  
Theoretical Contribution ◽  
Practical Guideline

The chapter intends to be a theoretical contribution for developers in the field of artificial intelligence. It also means a practical guideline for leaders, as decision-makers, to manage tasks and optimize performance. The proposed approach interprets the fluid nature of the decision-making process looking at knowledge and knowledge activities as dynamic, adaptive, and self-regulative, based not only on well-known explicit curricular goals but also on unpredictable interactions and relationships between players. The knowledge process is emerging in human and biological, social, and cultural environments.

A Locational Decision Making Framework for Shipbreaking under Multiple Criteria

2016 ◽  
Vol 7 (1) ◽  
pp. 76-97 ◽  
Author(s):  
Joshin John ◽  
Sushil Kumar
Keyword(s):  
Decision Making ◽  
Indian Subcontinent ◽  
Business Environment ◽  
Multiple Criteria ◽  
Decision Making Process ◽  
Multiple Factors ◽  
Locational Decision ◽  
Decision Making Framework ◽  
Work Done ◽  
Relevant Factors

The decision making process for shipbreaking is complicated and is dependent on multiple factors. However, due to the vastly unorganized nature of shipbreaking industry in major shipbreaking locations, there is little work done to the best of the authors' knowledge, wherein these factors are mapped, weighed and integrated in the form of a comprehensive decision making framework. In recent years, although there have been significant efforts by researchers to capture the process of shipbreaking and recycling in literature, a comprehensive decision support system that encapsulates the multiple criteria for shipbreaking in a quantifiable form, is yet to be developed. This paper attempts to bridge this gap, by formulating a decision making framework, particularly for selecting the shipbreaking facility and the extent of recycling subsequent to ship disassembly, using AHP methodology. The framework considers the relevant factors, and is useful not only for shipping companies and cash brokers for decision making, but also provides insights vis-à-vis the migrating pattern of shipbreaking industry, particularly from Indian subcontinent to China, as observed in the contemporary business environment.

scholarly journals Whose morality? Which rationality? Challenging artificial intelligence as a remedy for the lack of moral enhancement

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Silviya Serafimova
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Intelligent Agents ◽  
Machine Ethics ◽  
Decision Making Process ◽  
Human Beings ◽  
First Order ◽  
Moral Agents ◽  
Act Utilitarianism ◽  
Implicit And Explicit

Abstract Moral implications of the decision-making process based on algorithms require special attention within the field of machine ethics. Specifically, research focuses on clarifying why even if one assumes the existence of well-working ethical intelligent agents in epistemic terms, it does not necessarily mean that they meet the requirements of autonomous moral agents, such as human beings. For the purposes of exemplifying some of the difficulties in arguing for implicit and explicit ethical agents in Moor’s sense, three first-order normative theories in the field of machine ethics are put to test. Those are Powers’ prospect for a Kantian machine, Anderson and Anderson’s reinterpretation of act utilitarianism and Howard and Muntean’s prospect for a moral machine based on a virtue ethical approach. By comparing and contrasting the three first-order normative theories, and by clarifying the gist of the differences between the processes of calculation and moral estimation, the possibility for building what—one might call strong “moral” AI scenarios—is questioned. The possibility of weak “moral” AI scenarios is likewise discussed critically.

Artificial Intelligence in International Arbitration

2020 ◽  
pp. 590-616
Author(s):  
Myriam Gicquello
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Rule Of Law ◽  
Human Beings ◽  
International Arbitration ◽  
Naturalistic Decision Making ◽  
The Rule Of Law ◽  
Behavioural Analysis ◽  
International Adjudication ◽  
Definition Of

This chapter assesses the introduction of artificial intelligence in international arbitration. The contention is that it would not only reinstate confidence in the arbitral system—from the perspective of the parties and the general public—and participate in the development of the rule of law, but also engage with broader systemic considerations in enhancing its legitimacy, fairness, and efficiency. Yet, before addressing the why, what, and how of this proposition, a definition of artificial intelligence is warranted. It should be noted at the outset that this concept has a variety of meanings. Despite the lack of consensus on its meaning, the chapter will thus treat artificial intelligence as encompassing both semi-autonomous and autonomous computer systems dedicated to assisting or replacing human beings in decision-making tasks. It presents the conclusions of two extensive research programs respectively dealing with the performance of statistical models and naturalistic decision-making. From that behavioural analysis, the introduction of artificial intelligence in international arbitration be discussed against the general considerations of international adjudication and the specific goals pertaining to international arbitration.

Organizational Data Mining (ODM)

2008 ◽  
pp. 2289-2295 ◽  
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

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