# Participation Interfaces for Human-Centered AI

arXiv: https://arxiv.org/abs/2211.08419 · HTML: https://mlsanity.com/paper/2211.08419/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI](https://mlsanity.com/paper/2105.05424/) (2021)
- [Design and Analysis of the NIPS 2016 Review Process](https://mlsanity.com/paper/1708.09794/) (2017)
- [Envisioning Communities: A Participatory Approach Towards AI for Social Good](https://mlsanity.com/paper/2105.01774/) (2021)
- [The Limits of Global Inclusion in AI Development](https://mlsanity.com/paper/2102.01265/) (2021)

## Abstract (arXiv)

Emerging artificial intelligence (AI) applications often balance the preferences and impacts among diverse and contentious stakeholder groups. Accommodating these stakeholder groups during system design, development, and deployment requires tools for the elicitation of disparate system interests and collaboration interfaces supporting negotiation balancing those interests. This paper introduces interactive visual "participation interfaces" for Markov Decision Processes (MDPs) and collaborative ranking problems as examples restoring a human-centered locus of control.