The Value-Sensitive Conversational Agent Co-Design Framework
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
Conversational agents (CAs) are gaining traction in both industry and academia, especially with the advent of generative AI and large language models. As these agents are used more broadly by members of the general public and take on a number of critical use cases and social roles, it becomes important to consider the values embedded in these systems. This consideration includes answering questions such as 'whose values get embedded in these agents?' and 'how do those values manifest in the agents being designed?' Accordingly, the aim of this paper is to present the Value-Sensitive Conversational Agent (VSCA) Framework for enabling the collaborative design (co-design) of value-sensitive CAs with relevant stakeholders. Firstly, requirements for co-designing value-sensitive CAs which were identified in previous works are summarised here. Secondly, the practical framework is presented and discussed, including its operationalisation into a design toolkit. The framework facilitates the co-design of three artefacts that elicit stakeholder values and have a technical utility to CA teams to guide CA implementation, enabling the creation of value-embodied CA prototypes. Finally, an evaluation protocol for the framework is proposed where the effects of the framework and toolkit are explored in a design workshop setting to evaluate both the process followed and the outcomes produced.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 24 of its 228 references that are in the map. First posted on arXiv 18 Oct 2023.
Builds on
24 of 228 references.
References · 24
- 2023Towards Measuring the Representation of Subjective Global Opinions in Language Modelscited by 437
- 2019The Ethics of AI Ethics -- An Evaluation of Guidelinescited by 129
- 2020How do Data Science Workers Collaborate? Roles, Workflows, and Toolscited by 64
- 2019Requirements Engineering for Machine Learning: Perspectives from Data Scientistscited by 49
- 2022How Different Groups Prioritize Ethical Values for Responsible AIcited by 49
- 2017Society-in-the-Loop: Programming the Algorithmic Social Contractcited by 35
- 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAIcited by 33
- 2022Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practicecited by 32
- 2023DirectGPT: A Direct Manipulation Interface to Interact with Large Language Modelscited by 32
- 2021How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Studycited by 29
- 2021What are you optimizing for? Aligning Recommender Systems with Human Valuescited by 29
- 2022Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethicscited by 28
12 more
- 2021Hard Choices in Artificial Intelligencecited by 26
- 2023Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebookcited by 22
- 2023`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Valuescited by 18
- 2023Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practicecited by 15
- 2023Next Steps for Human-Centered Generative AI: A Technical Perspectivecited by 13
- 2021AI Ethics Principles in Practice: Perspectives of Designers and Developerscited by 12
- 2022Towards a multi-stakeholder value-based assessment framework for algorithmic systemscited by 12
- 2023Walking the Walk of AI Ethics: Organizational Challenges and the Individualization of Risk among Ethics Entrepreneurscited by 12
- 2019Enabling Value Sensitive AI Systems through Participatory Design Fictionscited by 11
- 2022Conversational Agents: Theory and Applicationscited by 8
- 2022Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startupscited by 4
- 2023Heads-Up Computing: Moving Beyond the Device-Centered Paradigmcited by 1
Built on by
Citing papers per year: 2026: 3.
3 papers.
- 2026Value-Sensitive AI for Prayer: Balancing the Agencies Between Human and AI Agents in Spiritual Contextcited by 0
- 2026Improving Survey Participation in Low-Literacy Populations Through Value-Sensitive Conversational AIcited by 0
- 2026Deflecting the Value Compass: Interacting with Large Language Models Temporarily Shifts Human Value Priorities Toward Personal Focuscited by 0
- 2026Deflecting the Value Compass: Interacting with Large Language Models Temporarily Shifts Human Value Priorities Toward Personal Focus22 Sep 2026
- 2026Improving Survey Participation in Low-Literacy Populations Through Value-Sensitive Conversational AI16 Jun 2026
- 2026Value-Sensitive AI for Prayer: Balancing the Agencies Between Human and AI Agents in Spiritual Context28 Apr 2026
Similar, not cited
Close in wording, no citation either way
- 2025The Value of Gen-AI Conversations: A bottom-up Framework for AI Value Alignment
- 2023Exploring Links between Conversational Agent Design Challenges and Interdisciplinary Collaboration
- 2024Should agentic conversational AI change how we think about ethics? Characterising an interactional ethics centred on respect
- 2022In conversation with Artificial Intelligence: aligning language models with human values
- 2025Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives
- 2025Conversational Alignment with Artificial Intelligence in Context
- 2023Synergizing Human-AI Agency: A Guide of 23 Heuristics for Service Co-Creation with LLM-Based Agents
- 2026Value-Sensitive Delegation in Everyday AI Agent Use: Evidence from OpenClaw