Crepe: A Mobile Screen Data Collector Using Graph QueryCollecting mobile screen information datasets remains challenging for academic researchers. Commercial organizations often have exclusive access to mobile data, leading to a “data monopoly” that restricts academic research and user transparency. Existing open-source mobile data collection frameworks primarily focus on mobile sensing data rather than screen content. We present Crepe, a no-code Android app that enables researchers to collect information displayed on screen through simple demonstrations of target data. Crepe utilizes a novel Graph Query technique, which augments mobile UI structures to support flexible identification, location, and collection of specific data pieces. The tool emphasizes participants' privacy and agency by providing full transparency over collected data and allowing easy opt-out. We designed and built Crepe for research purposes only and in scenarios where researchers obtain explicit consent from participants. Code for Crepe will be open-sourced to support future academic research data collection.2026YLYuwen Lu et al.University of Notre DameUser Research Methods (Interviews, Surveys, Observation)Computational Methods in HCIResearch Ethics & Open ScienceCHI
Compliant But Unsatisfactory: The Gap Between Auditing Standards and Practices for Probabilistic Genotyping SoftwareAI governance efforts increasingly rely on audit standards: agreed-upon practices for conducting audits. However, poorly designed standards can hide and lend credibility to inadequate systems. We explore how an audit standard’s design influences its effectiveness through a case study of ASB 018, a standard for auditing probabilistic genotyping software---software that the U.S. criminal legal system increasingly uses to analyze DNA samples. Through qualitative analysis of ASB 018 and five audit reports, we identify numerous gaps between the standard's desired outcomes and the auditing practices it enables. For instance, ASB 018 envisions that compliant audits establish restrictions on software use based on observed failures. However, audits can comply without establishing such boundaries. We connect these gaps to the design of the standard’s requirements such as vague language and undefined terms. We conclude with recommendations for designing audit standards and evaluating their effectiveness.2026AJAngela Jin et al.University of California, BerkeleyExplainable AI (XAI)Algorithmic Transparency & AuditabilityPrivacy by Design & User ControlCHI
Deaf and Hard of Hearing Access to Intelligent Personal Assistants: Comparison of Voice-Based Options with an LLM-Powered Touch InterfaceWe investigate intelligent personal assistants (IPAs) accessibility for deaf and hard of hearing (DHH) people who can use their voice in everyday communication. The inability of IPAs to understand diverse accents including deaf speech renders them largely inaccessible to non-signing and speaking DHH individuals. Using an Echo Show, we compared the usability of natural language input via two spoken English methods against that of a large language model (LLM)-assisted touch interface in a mixed-methods study. The two spoken English methods consisted of Alexa's built-in automatic speech recognition and a Wizard-of-Oz setting with a trained facilitator re-speaking commands. The touch method was navigated through an LLM-powered ‘task prompter,’ which integrated the user's history and smart environment to suggest contextually-appropriate commands. Quantitative results showed no significant differences across both spoken English conditions vs LLM-assisted touch. Qualitative results showed variability in opinions on the usability of each method. Ultimately, it will be necessary to have robust deaf-accented speech recognized natively by IPAs.2026PDPaige S DeVries et al.Gallaudet UniversityVoice AccessibilityIntelligent Voice Assistants (Alexa, Siri, etc.)Human-LLM CollaborationCHI
Co-Designing Multimodal Systems for Accessible Asynchronous Dance InstructionVideos make exercise instruction widely available, but they rely on visual demonstrations that blind and low vision (BLV) learners cannot see. While audio descriptions (AD) can make videos accessible, describing movements remains challenging as the AD must convey what to do (mechanics, location, orientation) and how to do it (speed, fluidity, timing). Prior work thus used multimodal instruction to support BLV learners with individual simple movements. However, it is unclear how these approaches scale to dance instruction with unique, complex movements and precise timing constraints. To inform accessible remote dance instruction systems, we conducted three co-design workshops (N=28) with BLV dancers, instructors, and experts in sound, haptics, and AD. Participants designed 8 systems revealing common themes: staged learning to dissect routines, crafting vocabularies for movements, and selectively using modalities—narration for movement structure, sound for expression, and haptics for spatial cues. We conclude with design implications to make learning dance accessible.2026UDUjjaini Das et al.University of Texas, AustinHaptic WearablesDance & Body Movement ComputingVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)CHI
Collaboration and Assistive Technology: Facilitating Joint Awareness for Noise SensitivityExisting research has explored various methods to support people with noise sensitivity (PWNS), from desensitization therapies to technological solutions. However, there is a gap in systems that identify and monitor characteristics of noise sensitivity experiences to help PWNS and their companions better understand their condition and make informed management decisions. To fill this gap, we developed AudioBuddy, an app with sensing and tracking features designed to promote awareness between PWNS and their companions. We tested AudioBuddy as a technological probe over a two-week field deployment. Our results show that AudioBuddy can support awareness of how sounds and environments influence the psychophysiological states of PWNS, aiding in understanding noise sensitivity experiences. Nonetheless, technical limitations impacted the depth of awareness participants could attain. We discuss challenges and opportunities for future systems to facilitate awareness among PWNS and their companions.2026EHEmani Hicks et al.University of California, IrvineEmotion-Sensing WearablesBehavior Change & Reflection TechnologyPrivacy & Data Ownership in Self-TrackingCHI
A11y-CUA Dataset: Characterizing the Accessibility Gap in Computer Use AgentsComputer Use Agents (CUAs) operate interfaces by pointing, clicking, and typing - mirroring interactions of sighted users (SUs) who can thus monitor CUAs and share control. CUAs do not reflect interactions by blind and low-vision users (BLVUs) who use assistive technology (AT). BLVUs thus cannot easily collaborate with CUAs. To characterize the accessibility gap of CUAs, we present A11y-CUA, a dataset of BLVUs and SUs performing 60 everyday tasks with 40.4 hours and 158,325 events. Our dataset analysis reveals that our collected interaction traces quantitatively confirm distinct interaction styles between SU and BLVU groups (mouse- vs.keyboard-dominant) and demonstrate interaction diversity within each group (sequential vs. shortcut navigation for BLVUs). We then compare collected traces to state-of-the-art CUAs under default and AT conditions (keyboard-only, magnifier). The default CUA executed 78.3% of tasks successfully. But with the AT conditions, CUA’s performance dropped to 41.67% and 28.3% with keyboard-only and magnifier conditions respectively, and did not reflect nuances of real AT use. With our open A11y-CUA dataset, we aim to promote collaborative and accessible CUAs for everyone.2026AMAnanya Gubbi Mohanbabu et al.The University of Texas at AustinVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)Motor Impairment Assistive Input TechnologiesUniversal & Inclusive DesignCHI
HistoryPalette: Supporting Exploration and Reuse of Past Alternatives in Image Generation and EditingCreative tasks require creators to iteratively produce, select, and discard potentially useful ideas. Now, creativity tools include generative AI features (e.g., Photoshop Generative Fill) that increase the number of alternatives creators consider through rapid experiments with prompts and random generations. Creators use tedious manual systems for organizing their prior ideas by saving file versions or hiding layers, but they lack the support they want for reusing prior alternatives in personal work or in communication with others. We present HistoryPalette, a system that supports exploration and reuse of prior designs in generative image creation and editing. Using HistoryPalette, creators and their collaborators explore a "palette" of prior design alternatives organized by spatial position, topic category, and creation time. HistoryPalette enables creators to quickly preview and reuse their prior work. In creative professional and client collaborator user studies, participants generated and edited images by exploring and reusing past design alternatives with HistoryPalette.2026KBKarim Benharrak et al.University of California, BerkeleyCreative Collaboration & Feedback SystemsPhotography & Image ProcessingVideo Production & EditingCHI