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Computational hermeneutics: evaluating generative AI as a cultural technology.

Cody Kommers1, Ruth Ahnert2, Maria Antoniak3

  • 1The Alan Turing Institute, London, United Kingdom.

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Summary
This summary is machine-generated.

Generative AI (GenAI) systems require evaluation that considers cultural context. Computational hermeneutics offers a new framework for understanding AI meaning-making, focusing on context rather than just accuracy.

Keywords:
GenAIcultureinterpretationmeaningsocietal impact

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Area of Science:

  • Artificial Intelligence
  • Humanities
  • Computational Linguistics

Background:

  • Generative AI (GenAI) systems are increasingly viewed as cultural technologies.
  • Current GenAI evaluation frameworks often overlook the fundamental role of culture in system operation.
  • Existing methods treat culture as a measurable variable rather than an intrinsic component.

Purpose of the Study:

  • To propose computational hermeneutics as a framework for evaluating GenAI systems.
  • To reframe GenAI evaluation by incorporating principles from hermeneutic theory.
  • To shift AI evaluation from accuracy metrics to contextual meaning.

Main Methods:

  • Drawing on hermeneutic theory to analyze GenAI functionality.
  • Identifying three core interpretive challenges for GenAI: situatedness, plurality, and ambiguity.
  • Developing principles for hermeneutic evaluation of AI systems.

Main Results:

  • GenAI systems function as "context machines" that inherently grapple with interpretation.
  • Computational hermeneutics provides an interpretive account of GenAI operations.
  • Proposed principles for evaluation include iterative benchmarks, human involvement, and context measurement.

Conclusions:

  • A hermeneutic approach is essential for understanding and improving GenAI systems.
  • Evaluating AI requires shifting focus from standardized accuracy to contextual meaning.
  • This perspective offers a new paradigm for designing and assessing contemporary AI.