What is responsible and sustainable data science?

Linnet Taylor*, Nadezhda Purtova

*Corresponding author for this work

Research output: Contribution to journalEditorialScientificpeer-review

Abstract

In the expansion of health ecosystems, issues of responsibility and sustainability of the data science involved are central. The idea that these values should be central to the practice of data science is increasingly gaining traction, yet there is no agreement on what exactly makes data science responsible or sustainable because these concepts prove slippery when applied to a global field involving commercial, academic and governmental actors. This lack of clarity is causing problems in setting goals and boundaries for data scientific practice, and risks fundamental disagreement on governance principles for this emerging field. We will argue in this commentary for a commons analytical framework as one approach to this problem, since it offers useful signposts for how to establish governance principles for shared resources.

Original languageEnglish
Pages (from-to)1-6
Number of pages6
JournalBig Data & Society
Volume6
Issue number2
DOIs
Publication statusPublished - Jul 2019

Keywords

  • Commons
  • health
  • responsibility
  • ethics
  • privacy
  • data protection
  • FRAMEWORK

Cite this

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title = "What is responsible and sustainable data science?",
abstract = "In the expansion of health ecosystems, issues of responsibility and sustainability of the data science involved are central. The idea that these values should be central to the practice of data science is increasingly gaining traction, yet there is no agreement on what exactly makes data science responsible or sustainable because these concepts prove slippery when applied to a global field involving commercial, academic and governmental actors. This lack of clarity is causing problems in setting goals and boundaries for data scientific practice, and risks fundamental disagreement on governance principles for this emerging field. We will argue in this commentary for a commons analytical framework as one approach to this problem, since it offers useful signposts for how to establish governance principles for shared resources.",
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What is responsible and sustainable data science? / Taylor, Linnet; Purtova, Nadezhda.

In: Big Data & Society, Vol. 6, No. 2, 07.2019, p. 1-6.

Research output: Contribution to journalEditorialScientificpeer-review

TY - JOUR

T1 - What is responsible and sustainable data science?

AU - Taylor, Linnet

AU - Purtova, Nadezhda

PY - 2019/7

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N2 - In the expansion of health ecosystems, issues of responsibility and sustainability of the data science involved are central. The idea that these values should be central to the practice of data science is increasingly gaining traction, yet there is no agreement on what exactly makes data science responsible or sustainable because these concepts prove slippery when applied to a global field involving commercial, academic and governmental actors. This lack of clarity is causing problems in setting goals and boundaries for data scientific practice, and risks fundamental disagreement on governance principles for this emerging field. We will argue in this commentary for a commons analytical framework as one approach to this problem, since it offers useful signposts for how to establish governance principles for shared resources.

AB - In the expansion of health ecosystems, issues of responsibility and sustainability of the data science involved are central. The idea that these values should be central to the practice of data science is increasingly gaining traction, yet there is no agreement on what exactly makes data science responsible or sustainable because these concepts prove slippery when applied to a global field involving commercial, academic and governmental actors. This lack of clarity is causing problems in setting goals and boundaries for data scientific practice, and risks fundamental disagreement on governance principles for this emerging field. We will argue in this commentary for a commons analytical framework as one approach to this problem, since it offers useful signposts for how to establish governance principles for shared resources.

KW - Commons

KW - health

KW - responsibility

KW - ethics

KW - privacy

KW - data protection

KW - FRAMEWORK

U2 - 10.1177/2053951719858114

DO - 10.1177/2053951719858114

M3 - Editorial

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