It has been the historic responsibility of the social sciences to investigate human societies. Fulfilling this responsibility requires social theories, measurement models and social data. Most existing theories and measurement models in the social sciences were not developed with the deep societal reach of algorithms in mind. The emergence of ‘algorithmically infused societies’—societies whose very fabric is co-shaped by algorithmic and human behaviour—raises three key challenges: the insufficient quality of measurements, the complex consequences of (mis)measurements, and the limits of existing social theories. Here we argue that tackling these challenges requires new social theories that account for the impact of algorithmic systems on social realities. To develop such theories, we need new methodologies for integrating data and measurements into theory construction. Given the scale at which measurements can be applied, we believe measurement models should be trustworthy, auditable and just. To achieve this, the development of measurements should be transparent and participatory, and include mechanisms to ensure measurement quality and identify possible harms. We argue that computational social scientists should rethink what aspects of algorithmically infused societies should be measured, how they should be measured, and the consequences of doing so.

研究人类社会一直是社会科学的历史责任。履行这一责任需要社会理论、测量模型和社会数据。社会科学中大多数现有的理论和测量模型都没有考虑到算法的深入社会影响。“算法注入社会”（其结构由算法和人类行为共同塑造的社会）的出现提出了三个关键挑战：测量质量不足、（错误）测量的复杂后果以及现有社会理论的局限性. 在这里，我们认为应对这些挑战需要新的社会理论来解释算法系统对社会现实的影响。为了发展这样的理论，我们需要新的方法将数据和测量整合到理论构建中。鉴于可以应用测量的规模，我们认为测量模型应该是可信赖的、可审计的和公正的。为了实现这一点，测量的开发应该是透明的和参与性的，并包括确保测量质量和识别可能危害的机制。我们认为，计算社会科学家应该重新考虑应该衡量算法注入社会的哪些方面，应该如何衡量，以及这样做的后果。并包括确保测量质量和识别可能危害的机制。我们认为，计算社会科学家应该重新考虑应该衡量算法注入社会的哪些方面，应该如何衡量，以及这样做的后果。并包括确保测量质量和识别可能危害的机制。我们认为，计算社会科学家应该重新考虑应该衡量算法注入社会的哪些方面，应该如何衡量，以及这样做的后果。