Ethical Frameworks for Conducting Social Challenge Studies
Sen, Protiva
Sen, Protiva
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Abstract
Computational social science research, particularly in online and networked environments, often involves exposing participants, communities, or technical systems to the adverse phenomena under study. Such designs include presenting misinformation in surveys, deploying automated bots on social media, submitting flawed patches to open-source projects, or exposing systems to adversarial attacks. We refer to these designs as social challenge studies, by analogy with medical challenge studies, where individuals are deliberately exposed to risk under controlled conditions. Unlike medical settings, however, social challenge studies typically occur in less controlled environments and with fewer clearly articulated ethical guidelines. Here, we examine whether ethical frameworks from medical challenge studies can inform ethical reasoning in computational social science. Our goal is not to replace existing frameworks, but to extend them by offering a structured way to reason about intentional exposure to risk, proportionality of harm, and responsibility for spillover effects on non-participants. We define social challenge studies as research that deliberately introduces structured risks, such as deception, exposure to harmful content, privacy loss, or disruption of social processes, to produce scientific knowledge. We examine three domains where such studies are prevalent: misinformation research, social media bot experiments, and cybersecurity research using honeypots. Across these contexts, intentional exposure raises questions about consent, deception, and the justification of harm. To address these challenges, we adapt principles from medical challenge studies into a framework tailored to social contexts. Key expectations include demonstrating scientific necessity, evaluating alternatives, determining appropriate consent mechanisms, assessing benefits and harms, minimizing risks, and protecting third parties. We also introduce decision trees to help researchers identify social challenge studies and select appropriate ethical safeguards. This work contributes a structured approach to evaluating research that intentionally introduces risk, supporting more transparent and accountable practices in computational social science.
Description
Date
1/1/2026
Student Status
Graduate Student
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Poster
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Computer Science
College/School
College of Engineering and Mathematical Sciences
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Social Science