# Enabling Value Sensitive AI Systems through Participatory Design Fictions

arXiv: https://arxiv.org/abs/1912.07381 · HTML: https://mlsanity.com/paper/1912.07381/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2019.

## Built on by (most cited first, 11 of 11)

- [Investigating Explainability of Generative AI for Code through Scenario-based Design](https://mlsanity.com/paper/2202.04903/) (2022)
- [Envisioning Communities: A Participatory Approach Towards AI for Social Good](https://mlsanity.com/paper/2105.01774/) (2021)
- [`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values](https://mlsanity.com/paper/2307.10221/) (2023)
- [Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems](https://mlsanity.com/paper/2402.16751/) (2024)
- [Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities](https://mlsanity.com/paper/2301.05753/) (2023)
- [What Makes An Expert? Reviewing How ML Researchers Define "Expert"](https://mlsanity.com/paper/2411.00179/) (2024)
- [The Value-Sensitive Conversational Agent Co-Design Framework](https://mlsanity.com/paper/2310.11848/) (2023)
- [Developing and Evaluating a Design Method for Positive Artificial Intelligence](https://mlsanity.com/paper/2402.01499/) (2024)
- [RelAItionship Building: Analyzing Recruitment Strategies for Participatory AI](https://mlsanity.com/paper/2508.20176/) (2025)
- [Identifying and Understanding Human Values in Text: A Tailorable LLM-based Architecture](https://mlsanity.com/paper/2605.27373/) (2026)
- [Deflecting the Value Compass: Interacting with Large Language Models Temporarily Shifts Human Value Priorities Toward Personal Focus](https://mlsanity.com/paper/2609.25586/) (2026)

## Abstract (arXiv)

Two general routes have been followed to develop artificial agents that are sensitive to human values---a top-down approach to encode values into the agents, and a bottom-up approach to learn from human actions, whether from real-world interactions or stories. Although both approaches have made exciting scientific progress, they may face challenges when applied to the current development practices of AI systems, which require the under-standing of the specific domains and specific stakeholders involved. In this work, we bring together perspectives from the human-computer interaction (HCI) community, where designing technologies sensitive to user values has been a longstanding focus. We highlight several well-established areas focusing on developing empirical methods for inquiring user values. Based on these methods, we propose participatory design fictions to study user values involved in AI systems and present preliminary results from a case study. With this paper, we invite the consideration of user-centered value inquiry and value learning.