United Kingdom ∙ Inadequacies in the UK’s Data Science Ethical Framework

2016 ◽  
Vol 2 (4) ◽  
pp. 555-560 ◽  
Author(s):  
C. Raab ◽  
R. Clarke
Keyword(s):  
United Kingdom ◽  
Data Science ◽  
Ethical Framework

scholarly journals Correction to: Gendered behavior as a disadvantage in open source software development

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Balazs Vedres ◽  
Orsolya Vasarhelyi
Keyword(s):  
United Kingdom ◽  
Software Development ◽  
Open Source ◽  
Open Source Software ◽  
Data Science ◽  
Open Source Software Development ◽  
Central European ◽  
University Of Oxford ◽  
European University

Following publication of the original article [1], we have been notified that one more affiliation of the corresponding author is missing. Currently Balasz Vedres affiliation is: 1 Oxford Internet Institute, University of Oxford, Oxford, United Kingdom It should be: 1 Oxford Internet Institute, University of Oxford, Oxford, United Kingdom; 2 Department of Network and Data Science, Central European University, Budapest, Hungary.

scholarly journals Data philanthropy and the design of the infraethics for information societies

2016 ◽  
Vol 374 (2083) ◽  
pp. 20160113 ◽  
Author(s):  
Mariarosaria Taddeo
Keyword(s):  
Data Science ◽  
Ethical Framework ◽  
Information Revolution ◽  
Private Companies ◽  
Governance Framework ◽  
Data Control ◽  
Share Data ◽  
Moral Problems ◽  
Future Information

In mature information societies, sharing data is increasingly recognized as a crucial means to foster their development. However, competing tensions on data control and ownership, limited technical understanding, and the lack of an adequate governance framework pose serious challenges to attempts to share data among different actors. Data philanthropy, understood as the donation of data from both individuals and private companies, has been proposed as means to meet these challenges. While at first sight data philanthropy may seem an uncontroversial phenomenon, a closer analysis reveals a bewildering network of problems. In this article, I analyse the role of data philanthropy in contemporary societies and the moral problems that it yields. I argue that the solution to these problems rests on the understanding of the infraethical nature of data philanthropy and on the design of an ethical framework encompassing the right infraethics and the right ethics. This is a framework able to address the changes brought about by the information revolution and to harness the opportunities that these pose for the prosperity of current and future information societies. This article is part of the themed issue ‘The ethical impact of data science’.

scholarly journals Data science ethics in government

2016 ◽  
Vol 374 (2083) ◽  
pp. 20160119 ◽  
Author(s):  
Cat Drew
Keyword(s):  
Data Science ◽  
New Technologies ◽  
Unintended Consequences ◽  
Ethical Framework ◽  
Social Good ◽  
Science Ethics ◽  
Policy Goals ◽  
Challenges And Opportunities ◽  
Government Data ◽  
The Government

Data science can offer huge opportunities for government. With the ability to process larger and more complex datasets than ever before, it can provide better insights for policymakers and make services more tailored and efficient. As with all new technologies, there is a risk that we do not take up its opportunities and miss out on its enormous potential. We want people to feel confident to innovate with data. So, over the past 18 months, the Government Data Science Partnership has taken an open, evidence-based and user-centred approach to creating an ethical framework. It is a practical document that brings all the legal guidance together in one place, and is written in the context of new data science capabilities. As part of its development, we ran a public dialogue on data science ethics, including deliberative workshops, an experimental conjoint survey and an online engagement tool. The research supported the principles set out in the framework as well as provided useful insight into how we need to communicate about data science. It found that people had a low awareness of the term ‘data science’, but that showing data science examples can increase broad support for government exploring innovative uses of data. But people's support is highly context driven. People consider acceptability on a case-by-case basis, first thinking about the overall policy goals and likely intended outcome, and then weighing up privacy and unintended consequences. The ethical framework is a crucial start, but it does not solve all the challenges it highlights, particularly as technology is creating new challenges and opportunities every day. Continued research is needed into data minimization and anonymization, robust data models, algorithmic accountability, and transparency and data security. It also has revealed the need to set out a renewed deal between the citizen and state on data, to maintain and solidify trust in how we use people's data for social good. This article is part of the themed issue ‘The ethical impact of data science’.

scholarly journals Ethics: To Do or Not To Do?

2018 ◽  
Author(s):  
Cheryl Trepanier ◽  
Ali Shiri ◽  
Toni Samek
Keyword(s):  
Professional Practice ◽  
Data Science ◽  
Code Of Ethics ◽  
Ethical Framework ◽  
Pratique Professionnelle ◽  
Information Professional ◽  
Other Information ◽  
Data Scientist ◽  
Code Of Professional Conduct ◽  
Science Association

This paper compares the 2012 International Federation of Library Associations and Institutions’ Code of Ethics for Librarians and Other Information Workers and the 2013 Data Science Association’s Data Science Code of Professional Conduct and discusses the disjuncture and related considerations that might strengthen practical understandings of the implications of ethics in library and information professional practice. This paper cautions against conflating a data scientist’s ethical framework with those of the traditional librarian and supports the development of a more robust framework for library and information ethics and a more comprehensive and inclusive framework for thinking about and conceptualizing data ethics.Ce document compare le Code de déontologie des bibliothécaires et des autres professionnels de l'information de 2012 de la Fédération internationale des associations de bibliothécaires et d'autres institutions, et le Code de déontologie des données scientifiques 2013 de la Data Science Association, et discute de la disjonction et des considérations connexes, l'éthique dans la pratique professionnelle des bibliothèques et de l'information. Cet article déconseille de confondre le cadre éthique d'un data scientist avec celui d’un bibliothécaire traditionnel et soutient le développement d'un cadre plus solide pour l'éthique des bibliothèques et de l'information et un cadre plus complet et inclusif pour penser et conceptualiser l'éthique des données.

An examination of IFLA and Data Science Association ethical codes

IFLA Journal ◽  
2019 ◽  
Vol 45 (4) ◽  
pp. 289-301
Author(s):  
Cheryl Trepanier ◽  
Ali Shiri ◽  
Toni Samek
Keyword(s):  
Professional Practice ◽  
Data Science ◽  
Code Of Ethics ◽  
Ethical Framework ◽  
Professional Conduct ◽  
Data Ethics ◽  
Information Professional ◽  
Other Information ◽  
Code Of Professional Conduct ◽  
Science Association

This paper compares the 2012 International Federation of Library Associations and Institutions’ Code of Ethics for Librarians and Other Information Workers and the 2013 Data Science Association’s Data Science Code of Professional Conduct and discusses the disjuncture and related considerations that might strengthen practical understandings of the implications of ethics in library and information professional practice. This paper cautions against conflating a data scientist’s ethical framework with those of the traditional librarian and supports the development of a more robust framework for library and information ethics and a more comprehensive and inclusive framework for thinking about and conceptualizing data ethics.

scholarly journals Our data, our society, our health: A vision for inclusive and transparent health data science in the United Kingdom and beyond

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Elizabeth Ford ◽  
Andy Boyd ◽  
Juliana K.F. Bowles ◽  
Alys Havard ◽  
Robert W. Aldridge ◽  
...  
Keyword(s):  
United Kingdom ◽  
Data Science ◽  
Health Data ◽  
The United Kingdom

scholarly journals Big Data for Public Domain: A bibliometric and visualized study of the scientific discourse during 2000–2020

2021 ◽  
Vol 5 (3) ◽  
pp. 220
Author(s):  
Prakoso Bhairawa Putera ◽  
Rostiena Pasciana
Keyword(s):  
Big Data ◽  
United Kingdom ◽  
Data Privacy ◽  
Data Science ◽  
Smart Cities ◽  
Open Data ◽  
The United States ◽  
Scientific Publications ◽  
Productive Author ◽  
The University

This article aims to investigate the trend of scientific publications under ‘big data and policy’ research during the last two decades, including the dynamics of the network structure of researchers and the institutions. Bibliometrics is utilized as a tool to reveal the dynamics of scientific discussions that occur through articles, published in international journals indexed/contained in the Scopus database; meanwhile, the analysis visualization is performed by using VOSviewer 1.6.16. The search results indicate that the United States serves as the country of origin for most productive author affiliations in publishing articles, the University of Oxford (United Kingdom) serves as the home institution for most productive author affiliations, and Williamson, B., from the University of Edinburgh (United Kingdom), is considered as the most prolific writer. In addition, the Swiss Sustainability Journal from MDPI is cited as the source for the most widely discussed publication topic in its journals. Further, ‘Big Data for Development: A Review of Promises and Challenges’ is regarded as the article with the most references. Additionally, the most discussed topics on ‘big data and policy’ include smart cities, open data, privacy, artificial intelligence, machine learning, and data science.

scholarly journals Engaging STEM Ethics Education

2018 ◽  
Vol 4 ◽  
pp. 1
Author(s):  
Kelly Ann Joyce ◽  
Kendall Darfler ◽  
Dalton George ◽  
Jason Ludwig ◽  
Kristene Unsworth
Keyword(s):  
Data Science ◽  
Ethics Education ◽  
Social Contexts ◽  
Ethical Framework ◽  
Original Research ◽  
Educational Materials ◽  
Science Data ◽  
Social Scientists ◽  
Computer Scientists ◽  
Data Validity

The automation of knowledge via algorithms, code and big data has brought new ethical concerns that computer scientists and engineers are not yet trained to identify or mediate. We present our experience of using original research to develop scenarios to explore how STS scholars can produce materials that facilitate ethics education in computer science, data science, and software engineering. STS scholars are uniquely trained to investigate the societal context of science and technology as well as the meaning STEM researchers attach to their day-to-day work practices. In this project, we use a collaborative, co-constitutive method of doing ethics education that focuses on building an ethical framework based on empirical practices, highlighting two issues in particular: data validity and the relations between data and inequalities. Through data-grounded scenario writing, we demonstrate how STS scholars and other social scientists can apply their expertise to the production of educational materials to spark broad ranging discussions that explore the connections between values, ethics, STEM, politics, and social contexts.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification
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