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.
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We thank the reviewers for their contributions to this work.
The authors declare no competing interests.
Peer review information Nature thanks Ceren Budak, Johan Ugander and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Wagner, C., Strohmaier, M., Olteanu, A. et al. Measuring algorithmically infused societies. Nature 595, 197–204 (2021). https://doi.org/10.1038/s41586-021-03666-1