What Is Responsible and Sustainable Data Science?

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 noagreement on what exactly makes data science responsible or sustainable because these concepts prove slippery whenapplied to a global field involving commercial, academic and governmental actors. This lack of clarity is causing problemsin setting goals and boundaries for data scientific practice, and risks fundamental disagreement on governance principlesfor this emerging field. We will argue in this commentary for a commons analytical framework as one approach to thisproblem, since it offers useful signposts for how to establish governance principles for shared resources

Focus: AI Ethics/Policy
Source: Big Data and Society
Readability: Expert
Type: Website Article
Open Source: No
Keywords: Commons, health, responsibility, ethics, privacy, data protection
Learn Tags: Ethics Fairness Framework Government
Summary: This article argues that a commons analytical framework is one approach to determining what makes data science responsible or sustainable, since it offers useful signposts for how to establish governance principles for shared resources.