Participation Interfaces for Human-Centered AI
This addresses the problem of stakeholder alignment in AI design for developers and users, but appears incremental as it builds on existing human-centered AI concepts.
The paper tackles the challenge of balancing preferences among diverse stakeholders in AI systems by introducing interactive visual 'participation interfaces' for Markov Decision Processes and collaborative ranking problems, aiming to restore human-centered control.
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.