Don't "research fast and break things": On the ethics of Computational Social Science

TL;DR

This paper discusses the ethical challenges in Computational Social Science (CSS) and proposes practical guardrails and habits of responsible research to ensure ethical, trustworthy, and responsible practices throughout the research lifecycle.

Contribution

It introduces a taxonomy of ethical challenges in CSS and advocates for integrating responsible research and innovation (RRI) practices into all research stages.

Findings

Identification of key ethical challenges in CSS

Practical steps for implementing RRI in CSS

Recommendations for stakeholder engagement and impact assessment

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.