scholarly journals Data deprivations, data gaps and digital divides: Lessons from the COVID-19 pandemic

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110255
Author(s):  
Wim Naudé ◽  
Ricardo Vinuesa
Keyword(s):  
Artificial Intelligence ◽  
Data Gathering ◽  
Analytical Techniques ◽  
Data Driven ◽  
Global Development ◽  
Limit Value ◽  
Digital Divides ◽  
Data Gaps ◽  
Development Outcomes ◽  
The Relationship

This paper draws lessons from the COVID-19 pandemic for the relationship between data-driven decision making and global development. The lessons are that (i) users should keep in mind the shifting value of data during a crisis, and the pitfalls its use can create; (ii) predictions carry costs in terms of inertia, overreaction and herding behaviour; (iii) data can be devalued by digital and data deluges; (iv) lack of interoperability and difficulty reusing data will limit value from data; (v) data deprivation, digital gaps and digital divides are not just a by-product of unequal global development, but will magnify the unequal impacts of a global crisis, and will be magnified in turn by global crises; (vi) having more data and even better data analytical techniques, such as artificial intelligence, does not guarantee that development outcomes will improve; (vii) decentralised data gathering and use can help to build trust – particularly important for coordination of behaviour.

scholarly journals Smart mobility, age and data justice

2020 ◽  
pp. 146144482090268 ◽  
Author(s):  
Maria Sourbati ◽  
Frauke Behrendt
Keyword(s):  
Public Service ◽  
Social Inclusion ◽  
Data Gathering ◽  
Data Driven ◽  
Policy Makers ◽  
Mobility Data ◽  
Strategic Approach ◽  
Data Gaps ◽  
Smart Mobility

This article examines converging trends in ageing, digitalisation and datafication in the context of mobility and transport. While mobility data are increasingly captured by (public) transport and mobility as a service (MaaS) providers, Internet of Things (IoT) vehicles, apps and so on, the increasing entanglement of mobility and datafication happens unevenly, for example, in relation to age. This is particularly significant in the light of the rise of data-driven policy-making, and its potential impacts on mobility provision for older people. The article highlights new questions for public policy around data gaps and social inclusion and examines them through a UK case study. The results show that old age and mobility is an area with significant gaps in the data available to policy makers. A key recommendation is for commissioning bodies to develop a strategic approach to structured data gathering and analysis that addresses issues of exclusion from smart public service infrastructure.

scholarly journals Demystification of Artificial Intelligence in Education – How much AI is really in the Educational Technology?

2020 ◽  
Vol 2 (1) ◽  
pp. 14 ◽  
Author(s):  
André Renz ◽  
Swathi Krishnaraja ◽  
Elisa Gronau
Keyword(s):  
Artificial Intelligence ◽  
Educational Technology ◽  
Learning Analytics ◽  
Public Discourse ◽  
Business Models ◽  
Data Driven ◽  
Current Status ◽  
Causal Connection ◽  
Artificial Intelligence In Education ◽  
The Relationship

<span lang="EN-US">The data-driven development of education through Learning Analytics in combination with Artificial Intelligence is an emerging field in the education sector. In the field of Artificial Intelligence in Education, numerous studies and research have been carried out over the past 60 years, and since then drastic changes have taken place. In the first part of this paper we present a brief overview of the current status of Learning Analytics and Artificial Intelligence in education. In order to develop a better understanding of the relationship between Learning Analytics and Artificial Intelligence in education, we outline the relationship between the two phenomena. The results show that the previous studies only vaguely distinguish between them: the terms are often used synonymously. In the second part of the paper we focus on the question why the European market currently has hardly any real applications for Artificial Intelligence in education. The research is based on a meta-investigation of data-driven business models, in particular the so-called Educational Technology providers. The core of the analysis is the question of how data-driven these companies really are, how much Learning Analytics and Artificial Intelligence is applied and whether there is a causal connection between the growth of the Educational Technology market and the application relevance of Artificial Intelligence in Education. In the scientific and public discourse, we can observe a distortion between the theoretical-conjunctive understanding of the application of Artificial Intelligence in Education and the current practical relevance.</span>

T is for territory: Gathering data about human experience in Developmental Transformations and advancing technologies

2020 ◽  
Vol 6 (2) ◽  
pp. 167-181 ◽  
Author(s):  
Jennifer Marshall
Keyword(s):  
Artificial Intelligence ◽  
Data Gathering ◽  
Human Experience ◽  
Drama Therapy ◽  
Arts Based Research ◽  
Developmental Transformations ◽  
The Relationship ◽  
Future Practice ◽  
Gathering Data

This article makes a comparison between developing technologies in the field of artificial intelligence (AI) and a practice used by drama therapists called Developmental Transformations (DvT). Both technologies gather granular data on human bodies; however, AI does so in the virtual realm, whereas DvT necessitates a physical encounter. As a contribution to theory, this article raises questions about whether interactions with technological interfaces are actual, virtual or somewhere in-between, and about where our bodies intersect in that dimensional landscape. Is it possible for practitioners of drama therapy, specifically DvT, to be in conversation with the growing dominance of technologies operated through AI, and where do the boundaries of human territory fit in relation to both? The relationship between these two approaches to data gathering are explored through the use of arts-based research in the form of collage. Possible implications for future practice as research are considered.

Management of Economics and State "Bottom"

2020 ◽  
Vol 17 (6) ◽  
pp. 76-91
Author(s):  
E. D. Solozhentsev
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Probabilistic Models ◽  
Human Life ◽  
Special Software ◽  
Human Quality ◽  
Relationship Of ◽  
The Relationship

The scientific problem of economics “Managing the quality of human life” is formulated on the basis of artificial intelligence, algebra of logic and logical-probabilistic calculus. Managing the quality of human life is represented by managing the processes of his treatment, training and decision making. Events in these processes and the corresponding logical variables relate to the behavior of a person, other persons and infrastructure. The processes of the quality of human life are modeled, analyzed and managed with the participation of the person himself. Scenarios and structural, logical and probabilistic models of managing the quality of human life are given. Special software for quality management is described. The relationship of human quality of life and the digital economy is examined. We consider the role of public opinion in the management of the “bottom” based on the synthesis of many studies on the management of the economics and the state. The bottom management is also feedback from the top management.

AI Narratives

2020 ◽  
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Ancient Greece ◽  
Historical Overview ◽  
Intelligent Machines ◽  
Narrative History ◽  
History Of ◽  
Imaginative Thinking ◽  
The Relationship ◽  
Insight Into

This book is the first to examine the history of imaginative thinking about intelligent machines. As real artificial intelligence (AI) begins to touch on all aspects of our lives, this long narrative history shapes how the technology is developed, deployed, and regulated. It is therefore a crucial social and ethical issue. Part I of this book provides a historical overview from ancient Greece to the start of modernity. These chapters explore the revealing prehistory of key concerns of contemporary AI discourse, from the nature of mind and creativity to issues of power and rights, from the tension between fascination and ambivalence to investigations into artificial voices and technophobia. Part II focuses on the twentieth and twenty-first centuries in which a greater density of narratives emerged alongside rapid developments in AI technology. These chapters reveal not only how AI narratives have consistently been entangled with the emergence of real robotics and AI, but also how they offer a rich source of insight into how we might live with these revolutionary machines. Through their close textual engagements, these chapters explore the relationship between imaginative narratives and contemporary debates about AI’s social, ethical, and philosophical consequences, including questions of dehumanization, automation, anthropomorphization, cybernetics, cyberpunk, immortality, slavery, and governance. The contributions, from leading humanities and social science scholars, show that narratives about AI offer a crucial epistemic site for exploring contemporary debates about these powerful new technologies.

The Oxford Handbook of Ethics of AI

2020 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Ethical Issues ◽  
Rapid Development ◽  
Data Driven ◽  
International Human Rights Law ◽  
Human Knowledge ◽  
Human Rights Law ◽  
Ai Ethics ◽  
And Behavior ◽  
Control Complex

This book explores the intertwining domains of artificial intelligence (AI) and ethics—two highly divergent fields which at first seem to have nothing to do with one another. AI is a collection of computational methods for studying human knowledge, learning, and behavior, including by building agents able to know, learn, and behave. Ethics is a body of human knowledge—far from completely understood—that helps agents (humans today, but perhaps eventually robots and other AIs) decide how they and others should behave. Despite these differences, however, the rapid development in AI technology today has led to a growing number of ethical issues in a multitude of fields, ranging from disciplines as far-reaching as international human rights law to issues as intimate as personal identity and sexuality. In fact, the number and variety of topics in this volume illustrate the width, diversity of content, and at times exasperating vagueness of the boundaries of “AI Ethics” as a domain of inquiry. Within this discourse, the book points to the capacity of sociotechnical systems that utilize data-driven algorithms to classify, to make decisions, and to control complex systems. Given the wide-reaching and often intimate impact these AI systems have on daily human lives, this volume attempts to address the increasingly complicated relations between humanity and artificial intelligence. It considers not only how humanity must conduct themselves toward AI but also how AI must behave toward humanity.

Humanism and Music

2020 ◽  
Author(s):  
Christopher M. Driscoll
Keyword(s):  
Artificial Intelligence ◽  
Popular Culture ◽  
Popular Music ◽  
Case Studies ◽  
Contemporary Music ◽  
Theoretical Relationship ◽  
Rock Band ◽  
Historical Developments ◽  
The Relationship

This chapter explores the relationship between humanism and music, giving attention to important theoretical and historical developments, before focusing on four brief case studies rooted in popular culture. The first turns to rock band Modest Mouse as an example of music as a space of humanist expression. Next, the chapter explores Austin-based Rock band Quiet Company and Westcoast rapper Ras Kass and their use of music to critique religion. Last, the chapter discusses contemporary popular music created by artificial intelligence and considers what non-human production of music suggests about the category of the human and, resultantly, humanism. These case studies give attention to the historical and theoretical relationship between humanism and music, and they offer examples of that relationship as it plays out in contemporary music.

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.

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