Three-dimensional (3D) data visualizations, such as surface plots, are vital in STEM fields from biomedical imaging to meteorology and spectroscopy, yet remain largely inaccessible to blind and low-vision (BLV) people. To address this gap, we conducted an Experience-Based Co-Design (EBCD) with BLV co-designers with expertise in non-visual data representations to create an accessible, multi-modal, web-native visualization tool. Using a multi-phase co-design methodology, our team of five BLV and one non-BLV researcher(s) participated in two iterative sessions, comparing a low-fidelity tactile probe with a high-fidelity digital prototype. This process produced a prototype with empirically grounded features, including reference sonification, stereo and volumetric audio, and configurable buffer aggregation, which our BLV co-designers validated as improving analytic accuracy and learnability. In this study, we explicitly target core analytic tasks essential for non-visual 3D data exploration: 3D orientation, landmark and peak finding, comparing local maxima versus global trends, gradient tracing, and identifying occluded or partially hidden features. Our work offers accessibility researchers and developers a co-design protocol for translating tactile knowledge to digital interfaces, concrete design guidance for future systems, and opportunities to extend accessible 3D visualization into embodied data environments.

Blind and low-vision (BLV) users remain largely excluded from three-dimensional (3D) surface and point data visualizations due to the reliance on visual interaction. Existing approaches inadequately support non-visual access, especially in browser-based environments. This study introduces DIXTRAL, a hosted web-native system, co-designed with BLV researchers to address these gaps through multimodal interaction. Conducted with two blind and one sighted researcher, this study took place over sustained design sessions. Data were gathered through iterative testing of the prototype, collecting feedback on spatial navigation, sonification, and usability. Co-design observations demonstrate that synchronized auditory, visual, and textual feedback, combined with keyboard and gamepad navigation, enhances both structure discovery and orientation. DIXTRAL aims to improve access to 3D continuous scalar fields for BLV users and inform best practices for creating inclusive 3D visualizations.

Moderating misinformation on social media is a complex task of warning users about potentially harmful content while remaining reliable, unbiased, and non-judgmental. Though this is a valid concern, it doesn't exempt platforms like TikTok from making their soft moderation interventions inaccessible for users who are low vision or blind. Through interviews with 13 low vision or blind TikTok users, we learned that this was exactly the case - the informative cues used for soft moderation were inaccessible in 93% of the cases. To address this participatory exclusion, our participants proposed redesigns for navigable informative cues through auditory means or "audio frictions" that both warn the users and provide them with contextual information on why a particular content might be misleading, false, or generally harmful.

Virtual reality (VR) has been celebrated for its immersive experiences, yet its potential for creating accessible and enjoyable environments for Blind and Low-Vision (BLV) individuals remains underexplored. Our project addresses this gap by developing a VR table tennis game specifically designed for BLV players. Utilizing an autoethnographic approach, our mixed-ability team, including three BLV co-designers, prototyped the game through rapid iterative testing and evaluation over four months. We integrated multi-sensory feedback mechanisms, such as spatial audio, haptic feedback, and high-contrast visuals, to enhance navigation and interaction. Our findings highlight the effectiveness of combining these modalities to create an enjoyable and realistic VR sports experience. However, we also identified challenges, such as the need for balanced sensory feedback to avoid overload. This study emphasizes the importance of inclusive design in VR gaming, offering new recreational opportunities for BLV individuals and setting the stage for future advancements in accessible VR technology.

This paper investigates how blind and low-vision (BLV) users interact with multimodal large language models (LLMs) to interpret data visualizations. Building upon our previous work on the multimodal access and interactive data representation (MAIDR) framework, our mixed-visual-ability team co-designed maidrAI, an LLM extension providing multiple AI responses to users’ visual queries. To explore generative AI-based data representation, we conducted user studies with 8 BLV participants, tasking them with interpreting box plots using our system. We examined how participants personalize LLMs through prompt engineering, their preferences for data visualization descriptions, and strategies for verifying LLM responses. Our findings highlight three dimensions affecting BLV users’ decision-making process: modal preference, LLM customization, and multimodal data representation. This research contributes to designing more accessible data visualization tools for BLV users and advances the understanding of inclusive generative AI applications.

Travel is an integral aspect of our lives, and this rings true for blind and visually impaired individuals alike. This activity can be enhanced with technology to facilitate a safer and more efficient experience, however with the abundance of options available it becomes difficult to establish a decision on which tools to use. In this autoethnography, I propose a technologically interdependent travel framework comprised of five pillars, orientation, communication, evaluation, navigation, and transportation. Based on over ten years of technology-supported travel experiences I have encountered first-hand as a visually impaired traveler, this experience report serves as a demonstration of what tools I have chosen and why, as well as how I utilize them throughout a naturalistic travel experience while associating each tool to a pillar from the proposed framework. I conclude this report with a recognition of existing limitations and opportunities for future research based on my observations and experiences.

Audio deepfakes are a form of deception where convincing speech sentences are synthesized through machine learning means to give an impression of a human speaker. Audio deepfakes emerge as an attractive vector for targeting users that rely on audio accessibility, such as individuals who are blind or low vision. The critical reliance on speech both as a medium and an affordance puts this population at an undue risk of being deceived as they rely solely on themselves to detect whether a piece of audio is a deepfake or not. To better understand the nature of this risk considering the nuanced reliance on assistive technologies such as screen readers, we conducted a user study with n=16 blind and low vision individuals from the US. Our participants achieved an overall discernment accuracy of 59%, and clips identified as deep fakes were only actually deepfakes in 50.8% of the cases (precision). The participants that self-identified as "low vision" performed slightly better (accuracy of 61%, precision of 64%) compared to the ones that self-identified as "blind" (accuracy of 55%, precision of 56%). Our qualitative results show that the participants in the "blind" group mostly considered a combination of infliction, imperfections in the voice, and the intensity in the speech delivery as discernment factors. The participants in the "low vision" group mostly used the speaker's pitch, enunciation, emotion, and the fluency and articulation of the speaker as discernment cues. Overall, participants felt that audio deepfakes have the potential to deceive visually impaired individuals with political disinformation, impersonate their voice in authentication and smart homes, and specifically target them with voice phishing and enhanced scams.

Dealing with misinformation on social media is a complex affair as platforms have to continuously decide whether and how to moderate falsehoods and misleading content. The options available are either hard moderation i.e., content and account removal or soft moderation i.e., substantiate false or misleading posts with misinformation warning labels. These warning labels are implemented as visual frictions with the intention to interrupt the user’s immersive experience and “nudge” them towards a better truth discernment. The choice of visual friction poses the question whether these warning labels are accessible for users who are low vision or blind. From the first accounts of 29 such users in our study, we learned that this is not the case. Excluded as such, the misinformation warning labels we tested on three platforms – Facebook, YouTube, and TikTok – did not help 72.4% of the visually impaired participants towards a better truth discernment. Our participants, therefore, provided useful and actionable recommendations for inclusive design of misinformation warnings that could meaningfully help the overall effort for curbing falsehoods and misleading statements.