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

As a quintessential social impact science, Computational Social Science (CSS) holds great promise to advance social justice, human flourishing, and biospheric sustainability. However, CSS is also an all-too-human science—conceived in particular social, cultural, and historical contexts and pursued amidst intractable power imbalances, structural inequities, and potential conflicts of interest. Its proponents must thus remain continuously self-critical about the role that values, interests, and power dynamics play in shaping mission-driven research. Likewise, they must take heed of the complicated social and historical conditions surrounding the generation and construction of data as well as the way that the activities and theories of CSS researchers can function to restructure and shape the phenomena that they purport only to measure and analyse. This article is concerned with setting up practical guardrails within the research activities and environments of CSS in response to these dilemmas. 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.