scholarly journals Enabling Trustworthiness in Artificial Intelligence - A Detailed Discussion

2015 ◽  
Vol 3 (2) ◽  
pp. 105-114 ◽  
Author(s):  
Siddhartha Vadlamudi ◽  
Keyword(s):  
Artificial Intelligence ◽  
Data Driven ◽  
Future Research ◽  
Specific Task ◽  
Maximum Capacity ◽  
Digital Devices ◽  
Distributed Ledger ◽  
Distributed Ledger Technology ◽  
Set Up ◽  
The Stability

Artificial intelligence (AI) delivers numerous chances to add to the prosperity of people and the stability of economies and society, yet besides, it adds up a variety of novel moral, legal, social, and innovative difficulties. Trustworthy AI (TAI) bases on the possibility that trust builds the establishment of various societies, economies, and sustainable turn of events, and that people, organizations, and societies can along these lines just at any point understand the maximum capacity of AI, if trust can be set up in its development, deployment, and use. The risks of unintended and negative outcomes related to AI are proportionately high, particularly at scale. Most AI is really artificial narrow intelligence, intended to achieve a specific task on previously curated information from a certain source. Since most AI models expand on correlations, predictions could fail to sum up to various populations or settings and might fuel existing disparities and biases. As the AI industry is amazingly imbalanced, and experts are as of now overpowered by other digital devices, there could be a little capacity to catch blunders. With this article, we aim to present the idea of TAI and its five essential standards (1) usefulness, (2) non-maleficence, (3) autonomy, (4) justice, and (5) logic. We further draw on these five standards to build up a data-driven analysis for TAI and present its application by portraying productive paths for future research, especially as to the distributed ledger technology-based acknowledgment of TAI.

scholarly journals Trustworthy artificial intelligence

2020 ◽  
Author(s):  
Scott Thiebes ◽  
Sebastian Lins ◽  
Ali Sunyaev
Keyword(s):  
Artificial Intelligence ◽  
Sustainable Development ◽  
Data Driven ◽  
Future Research ◽  
Full Potential ◽  
Research Framework ◽  
Distributed Ledger ◽  
Distributed Ledger Technology

Abstract Artificial intelligence (AI) brings forth many opportunities to contribute to the wellbeing of individuals and the advancement of economies and societies, but also a variety of novel ethical, legal, social, and technological challenges. Trustworthy AI (TAI) bases on the idea that trust builds the foundation of societies, economies, and sustainable development, and that individuals, organizations, and societies will therefore only ever be able to realize the full potential of AI, if trust can be established in its development, deployment, and use. With this article we aim to introduce the concept of TAI and its five foundational principles (1) beneficence, (2) non-maleficence, (3) autonomy, (4) justice, and (5) explicability. We further draw on these five principles to develop a data-driven research framework for TAI and demonstrate its utility by delineating fruitful avenues for future research, particularly with regard to the distributed ledger technology-based realization of TAI.

Exploring the Role of Complexity, Content and Individual Differences in Aesthetic Reactions to Semi-Abstract Art Photographs

2020 ◽  
Vol 8 (1) ◽  
pp. 89-119
Author(s):  
Nathalie Vissers ◽  
Pieter Moors ◽  
Dominique Genin ◽  
Johan Wagemans
Keyword(s):  
Large Scale ◽  
A Priori ◽  
Data Driven ◽  
Future Research ◽  
Response Patterns ◽  
Online Study ◽  
Exploratory Data ◽  
Science Collaboration ◽  
Set Up ◽  
Different Response

Artistic photography is an interesting, but often overlooked, medium within the field of empirical aesthetics. Grounded in an art–science collaboration with art photographer Dominique Genin, this project focused on the relationship between the complexity of a photograph and its aesthetic appeal (beauty, pleasantness, interest). An artistic series of 24 semi-abstract photographs that play with multiple layers, recognisability vs unrecognizability and complexity was specifically created and selected for the project. A large-scale online study with a broad range of individuals (n = 453, varying in age, gender and art expertise) was set up. Exploratory data-driven analyses revealed two clusters of individuals, who responded differently to the photographs. Despite the semi-abstract nature of the photographs, differences seemed to be driven more consistently by the ‘content’ of the photograph than by its complexity levels. No consistent differences were found between clusters in age, gender or art expertise. Together, these results highlight the importance of exploratory, data-driven work in empirical aesthetics to complement and nuance findings from hypotheses-driven studies, as they allow to go further than a priori assumptions, to explore underlying clusters of participants with different response patterns, and to point towards new venues for future research. Data and code for the analyses reported in this article can be found at https://osf.io/2fws6/.

scholarly journals Prospective applications of new technologies and artificial intelligence for systematizing the results of intellectual activity

2020 ◽  
Vol 224 ◽  
pp. 03018
Author(s):  
L Novoselova ◽  
E Grin
Keyword(s):  
Artificial Intelligence ◽  
Intellectual Property ◽  
New Technologies ◽  
Intellectual Activity ◽  
Distributed Ledger ◽  
Distributed Ledger Technology ◽  
Key Characteristics ◽  
Legal Structures ◽  
The Russian Federation ◽  
Ministry Of Higher Education

The article addresses the prospects of using distributed ledger technologies – blockchain and artificial intelligence – for the purpose of systematizing the rights to the results of intellectual activity for their subsequent commercialization. The authors describe the key characteristics of the distributed ledger technology and review various legal problems pertaining to the use of blockchain technologies. The authors draw conclusions regarding the prospects of using blockchain and artificial intelligence technologies as measures for rapid prevention and elimination of intellectual rights violations. They also express their views on the process of commercializing intellectual property and reducing the number of conflicts related to the inclusion of intellectual property objects into distributed ledger systems. The article was prepared with the financial support of the Ministry of Higher Education and Science of the Russian Federation within the framework of the research “Scientific and methodological support for the development of theoretical and applied legal structures (models) of accounting and disposal of rights to the results of intellectual activity (technology transfer)

scholarly journals Special issue: Informatics & data-driven medicine

2021 ◽  
Vol 18 (5) ◽  
pp. 6430-6433
Author(s):  
Ivan Izonin ◽  
◽  
Nataliya Shakhovska
Keyword(s):  
Artificial Intelligence ◽  
Experimental Studies ◽  
Vital Signs ◽  
Individual Case ◽  
Data Driven ◽  
Smart Devices ◽  
Huge Amount ◽  
Digital Devices ◽  
Steady Growth ◽  
Medicine Today

<abstract> <p>The current state of the development of Medicine today is changing dramatically. Previously, data of the patient's health were collected only during a visit to the clinic. These were small chunks of information obtained from observations or experimental studies by clinicians, and were recorded on paper or in small electronic files. The advances in computer power development, hardware and software tools and consequently design an emergence of miniature smart devices for various purposes (flexible electronic devices, medical tattoos, stick-on sensors, biochips etc.) can monitor various vital signs of patients in real time and collect such data comprehensively. There is a steady growth of such technologies in various fields of medicine for disease prevention, diagnosis, and therapy. Due to this, clinicians began to face similar problems as data scientists. They need to perform many different tasks, which are based on a huge amount of data, in some cases with incompleteness and uncertainty and in most others with complex, non-obvious connections between them and different for each individual patient (observation) as well as a lack of time to solve them effectively. These factors significantly decrease the quality of decision making, which usually affects the effectiveness of diagnosis or therapy. That is why the new concept in Medicine, widely known as Data-Driven Medicine, arises nowadays. This approach, which based on IoT and Artificial Intelligence, provide possibilities for efficiently process of the huge amounts of data of various types, stimulates new discoveries and provides the necessary integration and management of such information for enabling precision medical care. Such approach could create a new wave in health care. It will provide effective management of a huge amount of comprehensive information about the patient's condition; will increase the speed of clinician's expertise, and will maintain high accuracy analysis based on digital tools and machine learning. The combined use of different digital devices and artificial intelligence tools will provide an opportunity to deeply understand the disease, boost the accuracy and speed of its detection at early stages and improve the modes of diagnosis. Such invaluable information stimulates new ways to choose patient-oriented preventions and interventions for each individual case.</p> </abstract>

scholarly journals Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 16
Author(s):  
Abdul Majeed ◽  
Seong Oun Hwang
Keyword(s):  
Artificial Intelligence ◽  
Personal Data ◽  
General Purpose ◽  
Data Driven ◽  
Healthcare Sector ◽  
Future Research ◽  
Recent Developments ◽  
Technological Developments ◽  
Novel Coronavirus

This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.

scholarly journals Exploring the Next Wave of Blockchain and Distributed Ledger Technology: The Overlooked Potential of Scenario Analysis

2021 ◽  
Vol 13 (7) ◽  
pp. 183
Author(s):  
Horst Treiblmaier
Keyword(s):  
Scenario Analysis ◽  
Academic Research ◽  
Future Research ◽  
Future Developments ◽  
Current Trends ◽  
Distributed Ledger ◽  
Distributed Ledger Technology ◽  
Ideal Tool ◽  
Uncertain Future ◽  
The Ideal

Blockchain is predicted to disrupt industries, economies, and societies. The properties of distributed ledgers allow the creation of immutable data structures that facilitate shared access in real time and enable a plethora of innovative applications. However, blockchain is not a uniform technology but rather a bundle of evolving components whose implications are notoriously hard to predict. At present, it is not clear how current trends will evolve, with technical evolution, legislation, and public policy being three contingency factors that make ongoing disruptive transformations particularly hard to predict. In light of blockchain’s potential disruptive impact, it is surprising that scenario analysis has hitherto been largely ignored in academic research. Therefore, in this paper, we introduce the technique, clarify several misconceptions, and provide examples illustrating how this method can help to overcome the limitations of existing technology impact research. We conclude that if applied correctly, scenario analysis represents the ideal tool to rigorously explore uncertain future developments and to create a comprehensive foundation for future research.

scholarly journals The Future of Mobile Edge Computing

2021 ◽  
pp. 81-105
Author(s):  
Yan Zhang
Keyword(s):  
Artificial Intelligence ◽  
Edge Computing ◽  
Security And Privacy ◽  
Mobile Edge Computing ◽  
Distributed Ledger ◽  
Distributed Ledger Technology ◽  
Service Efficiency ◽  
The Future

AbstractThis chapter first introduces the fundamental principles of blockchain and the integration of blockchain and mobile edge computing (MEC). Blockchain is a distributed ledger technology with a few desirable security characteristics. The integration of blockchain and MEC can improve the security of current MEC systems and provide greater performance benefits in terms of better decentralization, security, privacy, and service efficiency. Then, the convergence of artificial intelligence (AI) and MEC is presented. A federated learning–empowered MEC architecture is introduced. To improve the performance of the proposed scheme, asynchronous federated learning is proposed. The integration of blockchain and federated learning is also presented to enhance the security and privacy of the federated learning–empowered MEC scheme. Finally, more MEC enabled applications are discussed.

scholarly journals Artificial Intelligence and Its Application in Optimization under Uncertainty

2021 ◽  
Author(s):  
Saeid Sadeghi ◽  
Maghsoud Amiri ◽  
Farzaneh Mansoori Mooseloo
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Optimization Under Uncertainty ◽  
Data Driven ◽  
Future Research ◽  
Intelligent Decision Support Systems ◽  
Data Driven Approach ◽  
Managerial Implications ◽  
Optimization Methodology ◽  
Conventional Optimization

Nowadays, the increase in data acquisition and availability and complexity around optimization make it imperative to jointly use artificial intelligence (AI) and optimization for devising data-driven and intelligent decision support systems (DSS). A DSS can be successful if large amounts of interactive data proceed fast and robustly and extract useful information and knowledge to help decision-making. In this context, the data-driven approach has gained prominence due to its provision of insights for decision-making and easy implementation. The data-driven approach can discover various database patterns without relying on prior knowledge while also handling flexible objectives and multiple scenarios. This chapter reviews recent advances in data-driven optimization, highlighting the promise of data-driven optimization that integrates mathematical programming and machine learning (ML) for decision-making under uncertainty and identifies potential research opportunities. This chapter provides guidelines and implications for researchers, managers, and practitioners in operations research who want to advance their decision-making capabilities under uncertainty concerning data-driven optimization. Then, a comprehensive review and classification of the relevant publications on the data-driven stochastic program, data-driven robust optimization, and data-driven chance-constrained are presented. This chapter also identifies fertile avenues for future research that focus on deep-data-driven optimization, deep data-driven models, as well as online learning-based data-driven optimization. Perspectives on reinforcement learning (RL)-based data-driven optimization and deep RL for solving NP-hard problems are discussed. We investigate the application of data-driven optimization in different case studies to demonstrate improvements in operational performance over conventional optimization methodology. Finally, some managerial implications and some future directions are provided.

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