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Updated: Oct 31, 2025

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Measuring algorithmically infused societies.

Claudia Wagner1,2,3, Markus Strohmaier4,5,6, Alexandra Olteanu7,8

  • 1GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany. claudia.wagner@gesis.org.

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|July 1, 2021
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Summary
This summary is machine-generated.

New social theories are needed to understand algorithmically infused societies. Developing trustworthy, auditable, and just measurement models requires transparent and participatory methods to ensure quality and mitigate harms.

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Area of Science:

  • Social Sciences
  • Computational Social Science
  • Sociology of Technology

Background:

  • Traditional social science theories and measurement models were not designed for the pervasive influence of algorithms.
  • The rise of 'algorithmically infused societies' presents novel challenges to understanding human behavior and social structures.
  • Existing frameworks struggle to account for the co-shaping of social realities by algorithmic and human actions.

Purpose of the Study:

  • To address the challenges posed by algorithmically infused societies, particularly concerning measurement quality and theoretical limitations.
  • To advocate for the development of new social theories that integrate the impact of algorithmic systems.
  • To propose requirements for trustworthy, auditable, and just measurement models in computational social science.

Main Methods:

  • Conceptual analysis of existing social science methodologies in the context of algorithmic influence.
  • Argument for the necessity of new theoretical frameworks and data integration methodologies.
  • Proposal for transparent and participatory development of measurement models, emphasizing quality assurance and harm identification.

Main Results:

  • Identified insufficient measurement quality, complex consequences of mismeasurement, and limitations of current social theories as key challenges.
  • Argued that new social theories are essential to understand and analyze algorithmically infused societies.
  • Stressed the need for measurement models that are trustworthy, auditable, and just, with transparent and participatory development processes.

Conclusions:

  • Computational social scientists must rethink measurement strategies in algorithmically infused societies.
  • Future research should focus on developing robust methodologies for integrating data into theory construction.
  • Prioritizing transparency, participation, and ethical considerations in measurement development is crucial for advancing social science research.