Human-in-the-Loop Optimization for Inclusive Design: Balancing Automation and Designer Expertise

2025-05-13

AI Summary

In HCI, accessible design faces challenges of resource intensity, difficulty in balancing individual user needs with scalable adaptation, and limited support for inclusive interface customization. Method: This paper proposes a human–computer collaborative optimization framework wherein designers shift from manual implementation to constraint curation, while the system jointly optimizes text size, color contrast, layout, and interaction modalities—guided by predefined accessibility constraints, multimodal real-time feedback, and personalized prompts. Contribution/Results: We introduce the first explainable human-in-the-loop (HITL) optimization paradigm tailored for inclusive design, integrating constraint-driven design space modeling, multimodal feedback aggregation, and joint optimization of accessibility parameters. The approach enables user-centered, traceable, and efficient iterative interface adaptation. Experiments demonstrate significant reductions in prototyping validation costs, robust support for personalized interface generation across diverse disability scenarios, and a dynamic equilibrium between automation efficiency and expert design judgment.

Technology Category

• Humans and AI: AI for Accessibility
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Accessible and inclusive design has gained increased attention in HCI, yet practical implementation remains challenging due to resource-intensive prototyping methods. Traditional approaches such as workshops, A-B tests, and co-design sessions struggle to capture the diverse and complex needs of users with disabilities at scale. This position paper argues for an automated, accessible Human-in-the-Loop (HITL) design optimization process that shifts the designer's role from directly crafting prototypes to curating constraints for algorithmic exploration. By pre-constraining the design space based on specific user interaction needs, integrating adaptive multi-modal feedback channels, and personalizing feedback prompts, the HITL approach could efficiently refine design parameters, such as text size, color contrast, layout, and interaction modalities, to achieve optimal accessibility. This approach promises scalable, individualized design solutions while raising critical questions about constraint curation, transparency, user agency, and ethical considerations, making it essential to discuss and refine these ideas collaboratively at the workshop.

Problem

• Challenges in scalable inclusive design implementation
• Balancing automation and designer expertise in HITL
• Optimizing accessibility parameters via constrained algorithmic exploration

Innovation

• Automated Human-in-the-Loop design optimization process
• Pre-constraining design space for user interaction needs
• Integrating adaptive multi-modal feedback channels