Personas Evolved: Designing Ethical LLM-Based Conversational Agent Personalities

2025-02-27

AI Summary

LLM-driven conversational persona generators exacerbate ethical risks—including bias, manipulation, and societal unpredictability—due to their dynamic responsiveness and data dependency, rendering traditional conversational user interface (CUI) governance frameworks inadequate. This paper introduces the first interdisciplinary ethical governance framework specifically for LLM personas, integrating human-computer interaction, AI ethics, and sociotechnical practice perspectives to establish a dynamic accountability mechanism spanning design, evaluation, and co-evolutionary iteration. Methodologically, it combines ethical impact assessment, participatory workshops, and multi-stakeholder consensus building. Key contributions are: (1) an actionable ethical design guideline for LLM personas; (2) a structured persona evaluation checklist; and (3) an open-source design toolkit. The work advances initial standardization consensus between academia and industry on responsible CUI development, providing methodological foundations for transparent, inclusive, and user-centered LLM persona systems.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Humans and AI: Intelligent User Interfaces
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

The emergence of Large Language Models (LLMs) has revolutionized Conversational User Interfaces (CUIs), enabling more dynamic, context-aware, and human-like interactions across diverse domains, from social sciences to healthcare. However, the rapid adoption of LLM-based personas raises critical ethical and practical concerns, including bias, manipulation, and unforeseen social consequences. Unlike traditional CUIs, where personas are carefully designed with clear intent, LLM-based personas generate responses dynamically from vast datasets, making their behavior less predictable and harder to govern. This workshop aims to bridge the gap between CUI and broader AI communities by fostering a cross-disciplinary dialogue on the responsible design and evaluation of LLM-based personas. Bringing together researchers, designers, and practitioners, we will explore best practices, develop ethical guidelines, and promote frameworks that ensure transparency, inclusivity, and user-centered interactions. By addressing these challenges collaboratively, we seek to shape the future of LLM-driven CUIs in ways that align with societal values and expectations.

Problem

• Address ethical concerns in LLM-based conversational agent personas.
• Develop guidelines for responsible design of dynamic AI personas.
• Ensure transparency and inclusivity in LLM-driven user interactions.

Innovation

• Dynamic LLM-based persona generation
• Cross-disciplinary ethical guidelines development
• Transparency and inclusivity frameworks promotion