scholarly journals Evidence of an artificial intelligence tool to augment data driven decisions for diabetes care in developing countries

2017 ◽  
Author(s):  
Shaikh Maaz
Keyword(s):  
Artificial Intelligence ◽  
Developing Countries ◽  
Diabetes Care ◽  
Data Driven ◽  
Data Driven Decisions

Against generalisation: Data-driven decisions need context to be human-compatible

2021 ◽  
pp. 026638212110619
Author(s):  
Sharon Richardson
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Theoretical Frameworks ◽  
The Past ◽  
Real World Applications ◽  
Contextual Framework ◽  
Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

Co-creating Value in People’s Interactions with Cognitive Assistants: A Service-System View

2018 ◽  
Vol 4 (2) ◽  
pp. 255-272 ◽  
Author(s):  
Md Abul Kalam Siddike ◽  
Youji Kohda
Keyword(s):  
Artificial Intelligence ◽  
Qualitative Research ◽  
Service System ◽  
Data Driven ◽  
Task Management ◽  
System Framework ◽  
Autonomous Agency ◽  
Cas A ◽  
Data Driven Decisions ◽  
Enhanced Learning

Purpose—The main purpose of this study was to develop a service-system framework in which people interact with cognitive assistants (CAs) for co-creation of value, such as enhanced communication and better task management. Methodology—Qualitative research was undertaken to deeply investigate and explore the value co-created through people’s interactions with CAs. A total of 32 interviews were conducted in three phases. The interview data were analysed using MAXQDA 12. Results—The results of this study indicate that most of the users use Apple’s Siri, Amazon Eco or Google Home as their CAs and that people’s interactions with CAs are influenced by their trust in and relative advantages of using CAs. The results also indicate that a diversity of value, such as enhanced communication, better task management, enhanced information retrieval, enhanced learning and better data-driven decisions, is co-created through interactions between people and CAs. Implications—We developed a service-system framework in which CAs are considered as actors and introduced the concept of ‘autonomous agency’ for controlling and coordinating people’s interactions with CAs. Originality—This is the first study on the value co-creation from people’s interactions with CAs (artificial-intelligence-based systems) by proposing a service-system framework in which CAs are considered as actors.

scholarly journals Artificial intelligence policy in India: a framework for engaging the limits of data-driven decision-making

2018 ◽  
Vol 376 (2133) ◽  
pp. 20180087 ◽  
Author(s):  
Vidushi Marda
Keyword(s):  
Artificial Intelligence ◽  
Data Model ◽  
Policy Development ◽  
Rapid Development ◽  
Data Driven ◽  
Social Good ◽  
Policy Deliberation ◽  
Potential Risks ◽  
Regional Influence ◽  
Data Driven Decisions

Artificial intelligence (AI) is an emerging focus area of policy development in India. The country's regional influence, burgeoning AI industry and ambitious governmental initiatives around AI make it an important jurisdiction to consider, regardless of where the reader of this article lives. Even as existing policy processes intend to encourage the rapid development of AI for economic growth and social good, an overarching trend persists in India, and several other jurisdictions: the limitations and risks of data-driven decisions still feature as retrospective considerations for development and deployment of AI applications. This article argues that the technical limitations of AI systems should be reckoned with at the time of developing policy, and the societal and ethical concerns that arise due to such limitations should be used toinformwhat policy processes aspire to achieve. It proposes a framework for such deliberation to occur, by analysing the three main stages of bringing machine learning (the most popular subset of AI techniques) to deployment—the data, model and application stage. It is written against the backdrop of India's current AI policy landscape, and applies the proposed framework to ongoing sectoral challenges in India. With a view to influence existing policy deliberation in the country, it focuses on potential risks that arise from data-driven decisions in general, and in the Indian context in particular.This article is part of the theme issue ‘Governing artificial intelligence: ethical, legal, and technical opportunities and challenges'.

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.

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 COVID-19 Brings Data Equity Challenges to the Fore

2021 ◽  
Author(s):  
H.V. Jagadish ◽  
Julia Stoyanovich ◽  
Bill Howe
Keyword(s):  
Quality Control ◽  
Government Policy ◽  
Data Driven ◽  
Contact Tracing ◽  
Control Mechanisms ◽  
Sources Of Information ◽  
Decision Systems ◽  
Physical Resources ◽  
Multiple Levels ◽  
Data Driven Decisions

The COVID-19 pandemic is compelling us to make crucial data-driven decisions quickly, bringing together diverse and unreliable sources of information without the usual quality control mechanisms we may employ. These decisions are consequential at multiple levels: they can inform local, state and national government policy, be used to schedule access to physical resources such as elevators and workspaces within an organization, and inform contact tracing and quarantine actions for individuals. In all these cases, significant inequities are likely to arise, and to be propagated and reinforced by data-driven decision systems. In this article, we propose a framework, called FIDES, for surfacing and reasoning about data equity in these systems.

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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