Computational social science research, particularly online studies, often involves exposing participants to the adverse phenomenon the researchers aim to study. Examples include presenting conspiracy theories in surveys, exposing systems to hackers, or deploying bots on social media. We refer to these as "social challenge studies," by analogy with medical research, where challenge studies advance vaccine and drug testing but also raise ethical concerns about exposing healthy individuals to risk. Medical challenge studies are guided by established ethical frameworks that regulate how participants are exposed to agents under controlled conditions. In contrast, social challenge studies typically occur with less control and fewer clearly defined ethical guidelines. In this paper, we examine the ethical frameworks developed for medical challenge studies and consider how their principles might inform social research. Our aim is to initiate discussion on formalizing ethical standards for social challenge studies and encourage long-term evaluation of potential harms.

翻译：计算社会科学研究，特别是线上研究，常涉及让参与者接触研究者旨在探究的不良现象。例如在调查中呈现阴谋论、将系统暴露给黑客攻击，或在社交媒体部署机器人。我们将其类比医学研究中的"挑战研究"，称之为"社会挑战研究"——医学挑战研究虽能推进疫苗和药物测试，但也因使健康个体暴露于风险而引发伦理关切。医学挑战研究遵循成熟的伦理框架，这些框架规范了如何在受控条件下使参与者接触病原体。相较之下，社会挑战研究通常在控制较弱、伦理准则不够明确的条件下进行。本文通过检视医学挑战研究已建立的伦理框架，探讨其原则如何为社会研究提供借鉴。我们旨在启动关于社会挑战研究伦理标准规范化的讨论，并推动对潜在危害的长期评估。