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Assessment of Social Interaction Behaviors
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AI for identifying social norm violation.

Yair Neuman1, Yochai Cohen2

  • 1The Functor Lab, Department of Cognitive and Brain Science, Ben-Gurion University of the Negev, 84105, Beersheba, Israel. yneuman@bgu.ac.il.

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Summary
This summary is machine-generated.

This study introduces computational methods to identify social norm violations using GPT-3 and psychological knowledge. The developed models accurately analyze complex social situations, demonstrating the power of modern computational tools.

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Area of Science:

  • Computational Social Science
  • Psychology
  • Artificial Intelligence

Background:

  • Identifying social norms and their violations presents a significant challenge in computational science.
  • Existing methods may struggle with the complexity of social dynamics and norm adherence.

Purpose of the Study:

  • To present a novel computational approach for identifying social norm violations.
  • To develop predictive models grounded in psychological knowledge for analyzing social behavior.

Main Methods:

  • Utilized GPT-3 for natural language understanding and zero-shot classification.
  • Employed automatic rule discovery to build predictive models.
  • Integrated psychological principles into computational frameworks.

Main Results:

  • Developed simple predictive models with significant performance on two large datasets.
  • Demonstrated the capability of computational tools to functionally analyze complex social situations.
  • Validated the models' effectiveness in identifying social norm violations.

Conclusions:

  • Modern computational tools, including AI like GPT-3, can effectively analyze complex social situations.
  • The proposed approach offers a viable method for identifying social norm violations in computational science.
  • Psychological knowledge is crucial for building accurate computational models of social behavior.