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Don’t 'Research Fast and Break Things': On the Ethics of Computational Social Science

Authors: Leslie, David;

Don’t 'Research Fast and Break Things': On the Ethics of Computational Social Science

Abstract

This article is concerned with setting up practical guardrails within the research activities and environments of CSS. It aims to provide CSS scholars, as well as policymakers and other stakeholders who apply CSS methods, with the critical and constructive means needed to ensure that their practices are ethical, trustworthy, and responsible. It begins by providing a taxonomy of the ethical challenges faced by researchers in the field of CSS. These are challenges related to (1) the treatment of research subjects, (2) the impacts of CSS research on affected individuals and communities, (3) the quality of CSS research and to its epistemological status, (4) research integrity, and (5) research equity. Taking these challenges as a motivation for cultural transformation, it then argues for the end-to-end incorporation of habits of responsible research and innovation (RRI) into CSS practices, focusing on the role that contextual considerations, anticipatory reflection, impact assessment, public engagement, and justifiable and well-documented action should play across the research lifecycle. In proposing the inclusion of habits of RRI in CSS practices, the chapter lays out several practical steps needed for ethical, trustworthy, and responsible CSS research activities. These include stakeholder engagement processes, research impact assessments, data lifecycle documentation, bias self-assessments, and transparent research reporting protocols.

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Keywords

Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Machine Learning, impact assessment, Computer Science - Computation and Language, research ethics, responsible research and innovation, research integrity, Computer Science - Social and Information Networks, computational social science, artificial intelligence, ethics, Machine Learning (cs.LG), Computer Science - Computers and Society, Computer Science - Computer Science and Game Theory, big data, research equity, Computers and Society (cs.CY), Computation and Language (cs.CL), Computer Science and Game Theory (cs.GT)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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4
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67
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