Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptions
arXiv:2507.15692v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) provide new opportunities for blind and low vision (BLV) people to access visual information in their daily lives. However, these models often produce errors that are difficult to detect without sight, posing safety and social risks in scenarios from medication identification to outfit selection. While BLV MLLM users use creative workarounds such as cross-checking between tools and consulting sighted individuals, these approaches are often time-consuming and impractical. We explore how systematically sur
The proliferation of MLLMs and their increasing application in daily assistive technologies for disabled populations makes the issue of 'perceived reliability' critical for user adoption and safety now.
Ensuring the reliability and interpretability of AI outputs, especially for vulnerable populations, is crucial for ethical AI development, public trust, and preventing potential harm, directly impacting regulatory frameworks and market acceptance.
The focus shifts from simply generating descriptions to actively calibrating user perception of those descriptions' accuracy, implying a new dimension in MLLM development and human-AI interaction design.
- · AI ethicists and safety researchers
- · Assistive technology developers
- · User experience (UX) designers focused on AI
- · Developers of uncalibrated MLLM solutions
- · Users relying solely on current MLLM outputs
Increased research and development into methods for MLLM reliability calibration and explainability for diverse user groups.
New industry standards and regulatory guidelines for MLLM deployment, particularly in critical accessibility applications, prioritizing transparency and error handling.
Enhanced public understanding and critical engagement with AI capabilities and limitations, fostering more realistic expectations and safer human-AI collaboration.
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