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Updated: Jan 9, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Ethics, generative AI and science communication.

Hannah R Feldman1, Fabien Medvecky1, Michelle Riedlinger2

  • 1Australian National University, Australia.

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|December 1, 2025
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Summary
This summary is machine-generated.

Generative artificial intelligence (AI) in science communication requires careful ethical review. Examining outputs, impacts, and narratives is crucial for responsible AI integration in communicating science.

Keywords:
artificial intelligenceethicsscience communicationstudies of science and technology

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Area of Science:

  • Science Communication
  • Artificial Intelligence Ethics

Background:

  • Generative AI (GenAI) applications in science communication have faced swift, negative public reactions.
  • Existing literature on GenAI in related fields and ethics of science communication informs this analysis.

Purpose of the Study:

  • To map key ethical issues of GenAI in science communication.
  • To explore three dimensions of ethical concerns: outputs, impacts, and narratives.

Main Methods:

  • Literature review of GenAI applications and ethics in science communication.
  • Analysis of GenAI as a complex system beyond mere output generation.

Main Results:

  • GenAI in science communication presents ethical challenges.
  • Three critical areas for ethical exploration are outputs, social/environmental impacts, and AI narratives.

Conclusions:

  • Responsible integration of GenAI in science communication necessitates thorough ethical consideration.
  • Addressing outputs, impacts, and narratives is vital for socially and ethically acceptable AI use.