Designing AI for Real Users -- Accessibility Gaps in Retail AI Front-End

2026-03-30

AI Summary

Current retail AI frontends commonly operate under an “ideal user” assumption, overlooking the perceptual, motor, and cognitive differences of users with disabilities and diverse needs, thereby creating systemic accessibility gaps. This study employs qualitative analysis of representative applications—including virtual assistants, virtual try-on systems, and hyper-personalized recommendation engines—to uncover interaction design mechanisms that inadvertently exclude atypical users. It identifies the root cause not in technical limitations but in procurement and organizational processes that lack accessibility mandates. To address this, the work proposes a “front-end assurance” framework that aligns the multimodal capabilities and intelligence claims of AI systems with the genuine diversity of end users, filling a critical gap in existing AI governance regarding user experience and advancing the practical implementation of inclusive design in commercial contexts.

Technology Category

• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Ad search and search for Web retail

Abstract

As AI becomes embedded in customer-facing systems, ethical scrutiny has largely focused on models, data, and governance. Far less attention has been paid to how AI is experienced through user-facing design. This commentary argues that many AI front-ends implicitly assume an 'ideal user body and mind', and that this becomes visible and ethically consequential when examined through the experiences of differently abled users. We explore this through retail AI front-ends for customer engagement - i.e., virtual assistants, virtual try-on systems, and hyper-personalised recommendations. Despite intuitive and inclusive framing, these systems embed interaction assumptions that marginalise users with vision, hearing, motor, cognitive, speech and sensory differences, as well as age-related variation in digital literacy and interaction norms. Drawing on practice-led insights, we argue that these failures persist not primarily due to technical limits, but due to the commercial, organisational, and procurement contexts in which AI front-ends are designed and deployed, where accessibility is rarely contractual. We propose front-end assurance as a practical complement to AI governance, aligning claims of intelligence and multimodality with the diversity of real users.

Problem

• AI accessibility
• inclusive design
• retail AI
• user experience
• disability inclusion

Innovation

• accessibility
• AI front-end design
• inclusive AI
• front-end assurance
• user diversity