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The increasing use of Generative Artificial Intelligence (AI) systems in various contexts has raised ethical concerns about their impacts on society. These systems, which generate texts, images, and structured information, offer automation and efficiency gains, but also pose risks such as the spread of misinformation, reinforcement of biases, and privacy violations. This study analyzes how popular generative AI systems communicate ethical issues to their users. To this end, we performed a systematic analysis of ChatGPT, Gemini, and Claude, using the Semiotic Inspection Method combined with an epistemic tool based on Semiotic Engineering. The ethical principles of Beneficence, Nonmaleficence, Autonomy, Justice, and Explainability guided the analysis. The results indicate the need for more transparent and coherent ethical communication in generative AI design. Our study contributes to articulating the design space of these systems and the challenges of incorporating ethical values and principles. In addition, the methodology and results offer contributions to research in ethical generative AI technologies.

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Generative AI Strategies to Communicate Ethical Considerations to Users

  • 会议论文

  • 发表日期：

    2026年

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  • João Carlos Santana Silveira,
  • Libiane Comes,
  • Helena Cristo Martins,
  • Cristiane Aparecida Lana,
  • Maria Lúcia Bento Villela

摘要

The increasing use of Generative Artificial Intelligence (AI) systems in various contexts has raised ethical concerns about their impacts on society. These systems, which generate texts, images, and structured information, offer automation and efficiency gains, but also pose risks such as the spread of misinformation, reinforcement of biases, and privacy violations. This study analyzes how popular generative AI systems communicate ethical issues to their users. To this end, we performed a systematic analysis of ChatGPT, Gemini, and Claude, using the Semiotic Inspection Method combined with an epistemic tool based on Semiotic Engineering. The ethical principles of Beneficence, Nonmaleficence, Autonomy, Justice, and Explainability guided the analysis. The results indicate the need for more transparent and coherent ethical communication in generative AI design. Our study contributes to articulating the design space of these systems and the challenges of incorporating ethical values and principles. In addition, the methodology and results offer contributions to research in ethical generative AI technologies.