scholarly journals Ethical Applications of Big Data-Driven AI on Social Systems: Literature Analysis and Example Deployment Use Case

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 235
Author(s):  
Paulo Garcia ◽  
Francine Darroch ◽  
Leah West ◽  
Lauren BrooksCleator
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Social Issues ◽  
Social Computing ◽  
Social Systems ◽  
Human Interaction ◽  
Data Driven ◽  
Vulnerable Groups ◽  
Use Case ◽  
Ongoing Research

The use of technological solutions to address the production of goods and offering of services is ubiquitous. Health and social issues, however, have only slowly been permeated by technological solutions. Whilst several advances have been made in health in recent years, the adoption of technology to combat social problems has lagged behind. In this paper, we explore Big Data-driven Artificial Intelligence (AI) applied to social systems; i.e., social computing, the concept of artificial intelligence as an enabler of novel social solutions. Through a critical analysis of the literature, we elaborate on the social and human interaction aspects of technology that must be in place to achieve such enabling and address the limitations of the current state of the art in this regard. We review cultural, political, and other societal impacts of social computing, impact on vulnerable groups, and ethically-aligned design of social computing systems. We show that this is not merely an engineering problem, but rather the intersection of engineering with health sciences, social sciences, psychology, policy, and law. We then illustrate the concept of ethically-designed social computing with a use case of our ongoing research, where social computing is used to support safety and security in home-sharing settings, in an attempt to simultaneously combat youth homelessness and address loneliness in seniors, identifying the risks and potential rewards of such a social computing application.

Impact of Big Data and Artificial Intelligence on Industry: Developing a Workforce Roadmap for a Data Driven Economy

2021 ◽  
Author(s):  
Marina Johnson ◽  
Rashmi Jain ◽  
Peggy Brennan-Tonetta ◽  
Ethne Swartz ◽  
Deborah Silver ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Driven

scholarly journals Out of the loop? On the radical and the routine in urban big data

Urban Studies ◽  
2021 ◽  
pp. 004209802110140
Author(s):  
Sarah Barns
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Computational Models ◽  
Data Driven ◽  
Urban Systems ◽  
Out Of The Loop ◽  
Urban Futures ◽  
Urban Behaviour ◽  
The City

This commentary interrogates what it means for routine urban behaviours to now be replicating themselves computationally. The emergence of autonomous or artificial intelligence points to the powerful role of big data in the city, as increasingly powerful computational models are now capable of replicating and reproducing existing spatial patterns and activities. I discuss these emergent urban systems of learned or trained intelligence as being at once radical and routine. Just as the material and behavioural conditions that give rise to urban big data demand attention, so do the generative design principles of data-driven models of urban behaviour, as they are increasingly put to use in the production of replicable, autonomous urban futures.

scholarly journals Artificial Intelligence in Education

Seminar.net ◽  
2021 ◽  
Vol 17 (2) ◽  
Author(s):  
Xavier Giró Gràcia ◽  
Juana M. Sancho-Gil
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Digital Technology ◽  
Social Systems ◽  
Machine Learning Algorithms ◽  
Educational Systems ◽  
Negative Effects ◽  
Black Boxes ◽  
Artificial Intelligence In Education

Digital technology is constantly permeating and transforming all social systems, and education is not an exception. In the last decade, the unstoppable development of Artificial Intelligence, based on machine learning algorithms and fuelled by Big Data, has given a new push to the hope of improving learning-based machines, and providing educational systems with ‘effective’ solutions. Educators, educational researchers and policymakers, in general, lack the knowledge and expertise to understand the underlying logic of these new ‘black boxes’, and we do not have sufficient research-based evidence to understand the consequences that an excessive use of screens has in students’ development. This paper first discusses the notions behind what Big Data is and what it means in our current society; how data is the new currency that has driven the use of algorithms in all areas of our society, and specifically in the field of Artificial Intelligence; and the concept of ‘black boxes’, and its possible impact on education. Then, it discusses the underlying educational discourses, pointing out the need to analyse not only their contributions but also their possible negative effects. It finishes with considerations and a proposed agenda for further studying this phenomenon.

scholarly journals Unlocking the Power of Artificial Intelligence and Big Data in Medicine

10.2196/16607 ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. e16607 ◽  
Author(s):  
Christian Lovis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Global Environment ◽  
Data Driven ◽  
Cross Linking ◽  
Science And Society ◽  
Slowing Down

Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and a mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

scholarly journals A Data-Driven Platform to Computer Performance Analysis and Recommendation using AI and Big Data Analysis

2021 ◽  
Author(s):  
Shuo Chen ◽  
Yu Sun
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Qualitative Evaluation ◽  
Cost Effective ◽  
Big Data Analysis ◽  
Data Driven ◽  
Computer Performance ◽  
Time And Energy ◽  
Artificial Intelligence Application ◽  
Intelligence Application

When I was assembling the computer, I found a problem. This problem is that we need to spend a lot of time and energy when we choose a desktop with a configuration and price that we are satisfied with [5]. Some computer websites will only recommend some ordinary desktops to users. Does not allow users to get what they really want, and some other shops that assemble computer mainframes use the characteristics of customers that do not understand computers to increase prices. So I wanted to create a software to help these people who need to assemble a computer to find the most suitable computer efficiently and in accordance with their requirements [6]. This program, according to the needs of users, artificial intelligence application crawler technology can help users find the most suitable computer parts based on big data, and help users get the most cost-effective self-assembled computer host. We applied our application to match a person in need of a computer host with My Platform and conducted a qualitative evaluation of the method [7]. The results showed that My Platform can efficiently and quality match the user's needs and find the best solution for the user.

Applying emerging data-driven technologies in social security. Country experiences and ISSA guidelines

2021 ◽  
Vol 150 (3) ◽  
Author(s):  
Raul Ruggia-Frick
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Social Security ◽  
Data Driven ◽  
Social Security Systems ◽  
Security Systems ◽  
Current Application ◽  
Security Services ◽  
Development Processes ◽  
Operational Processes

The application of ICT is enabling the implementation of increasingly comprehensive social security systems throughout the world as well as the transformation of social security services. In particular, the so-called data-driven innovation enables social security institutions to improve products, processes and organisational methods. In this line, social security institutions are progressively applying emerging technologies, such as Analytics, Big Data, and Artificial Intelligence. While the pairing of analytics and big data allows for the performing of sophisticated analyses on increasingly large databases, Artificial Intelligence enables for automating processes and assisting staff in tasks requiring human decisions. However, the application of such emerging data-driven technologies brings with it many challenges, mainly the complexities of combining the adoption of not fully tested technologies with the required stability of critical operational processes and differences in the application of development processes. This paper addresses these issues and presents an overview of emerging data-driven technologies and their current application in social security institutions. It also presents guidelines supporting the application of data-driven technologies in social security developed by the International Social Security Association (ISSA).

scholarly journals Smart Cities and Big Data Analytics: A Data-Driven Decision-Making Use Case

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 286-313
Author(s):  
Ahmed M. Shahat Osman ◽  
Ahmed Elragal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Smart Cities ◽  
Research Question ◽  
Big Data Analytics ◽  
Data Driven ◽  
Use Case ◽  
Data Driven Decision Making ◽  
Domain Independent

Interest in smart cities (SCs) and big data analytics (BDA) has increased in recent years, revealing the bond between the two fields. An SC is characterized as a complex system of systems involving various stakeholders, from planners to citizens. Within the context of SCs, BDA offers potential as a data-driven decision-making enabler. Although there are abundant articles in the literature addressing BDA as a decision-making enabler in SCs, mainstream research addressing BDA and SCs focuses on either the technical aspects or smartening specific SC domains. A small fraction of these articles addresses the proposition of developing domain-independent BDA frameworks. This paper aims to answer the following research question: how can BDA be used as a data-driven decision-making enabler in SCs? Answering this requires us to also address the traits of domain-independent BDA frameworks in the SC context and the practical considerations in implementing a BDA framework for SCs' decision-making. This paper's main contribution is providing influential design considerations for BDA frameworks based on empirical foundations. These foundations are concluded through a use case of applying a BDA framework in an SC's healthcare setting. The results reveal the ability of the BDA framework to support data-driven decision making in an SC.

scholarly journals Unlocking the Power of Artificial Intelligence and Big Data in Medicine (Preprint)

2019 ◽  
Author(s):  
Christian Lovis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Global Environment ◽  
Data Driven ◽  
Cross Linking ◽  
Science And Society ◽  
Slowing Down

UNSTRUCTURED Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound, with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

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