Measuring algorithmically infused societies
Abstract
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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Observable behavior on digital media results from interactive feedback loops between human behavior, algorithms, and other platform features.
“The eventually observable behavior thus results from interactive feedback loops between human behavior, algorithms, and other platform features (Narayanan, 2023b; Tsvetkova et al., 2017; Wagner et al., 2021).”
From Social Drivers and Algorithmic Mechanisms on Digital Media. (2024) · cites this paper as Wagner et al., 2021
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- Authors
- Wagner, C., Strohmaier, M., Olteanu, A., Kıcıman, E., Contractor, N., & Eliassi-Rad, T.
- Affiliations
- GESIS - Leibniz Institute for the Social Sciences (DE)
- Microsoft Research New York City (United States) (US)
- Microsoft (United States) (US)
- Northwestern University (US)
- Corresponding author
- Wagner, Claudia
- Venue
- Nature
- Published
- 2021-06-30
- Volume
- 595
- Issue
- 7866
- Pages
- 197-204
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W3175319024
- Cited by
- 144
- References
- 35
- Citations by year
- 2026: 16 · 2025: 32 · 2024: 15 · 2023: 46 · 2022: 22 · 2021: 13
- FWCI
- 30.01
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Social Sciences › General Social Sciences › Social Science and Policy Research
- Keywords
- measurement of social phenomena, measurement model, social theory, algorithmic systems, measurement quality, transparency, computational social science
- MeSH terms
- Algorithms, Computer Simulation, Humans, Politics, Social Conditions, Social Sciences, Guidelines as Topic, Datasets as Topic
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