About

Leah Findlater is a professor in the Department of Human Centered Design & Engineering at the University of Washington. Her specialization includes accessible computing, mobile and wearable technologies, human-centered AI, and human-computer interaction. Her research focuses on designing technologies that improve accessibility and usability for diverse populations, emphasizing human-centered approaches to AI and mobile device interaction.

Research topics

• Computer Science
• Psychology
• Computer Security
• Human–computer interaction
• Artificial Intelligence
• Engineering
• Optometry
• Internet privacy
• Geography
• Applied psychology
• Mathematics
• Multimedia
• Cognitive psychology

Selected publications

• Bootstrapping Sign Language Annotations with Sign Language Models

  arXiv (Cornell University) · 2026-04-08

  preprintOpen accessSenior author

  AI-driven sign language interpretation is limited by a lack of high-quality annotated data. New datasets including ASL STEM Wiki and FLEURS-ASL contain professional interpreters and 100s of hours of data but remain only partially annotated and thus underutilized, in part due to the prohibitive costs of annotating at this scale. In this work, we develop a pseudo-annotation pipeline that takes signed video and English as input and outputs a ranked set of likely annotations, including time intervals, for glosses, fingerspelled words, and sign classifiers. Our pipeline uses sparse predictions from our fingerspelling recognizer and isolated sign recognizer (ISR), along with a K-Shot LLM approach, to estimate these annotations. In service of this pipeline, we establish simple yet effective baseline fingerspelling and ISR models, achieving state-of-the-art on FSBoard (6.7% CER) and on ASL Citizen datasets (74% top-1 accuracy). To validate and provide a gold-standard benchmark, a professional interpreter annotated nearly 500 videos from ASL STEM Wiki with sequence-level gloss labels containing glosses, classifiers, and fingerspelling signs. These human annotations and over 300 hours of pseudo-annotations are being released in supplemental material.

  PublisherDOI

• Hierarchical Instance Tracking to Balance Privacy Preservation with Accessible Information

  2026-03-06

  articleOpen access

  We propose a novel task, hierarchical instance tracking, which entails tracking all instances of predefined categories of objects and parts, while maintaining their hierarchical relationships. We introduce the first benchmark dataset supporting this task, consisting of 2,765 unique entities that are tracked in 552 videos and belong to 40 categories (across objects and parts). Evaluation of seven variants of four models tailored to our novel task reveals the new dataset is challenging. Our dataset is available at https://vizwiz.org/tasks-and-datasets/hierarchical-instance-tracking/

  PublisherOA PDFDOI

• Bootstrapping Sign Language Annotations with Sign Language Models

  arXiv (Cornell University) · 2026-04-08

  articleOpen accessSenior author

  AI-driven sign language interpretation is limited by a lack of high-quality annotated data. New datasets including ASL STEM Wiki and FLEURS-ASL contain professional interpreters and 100s of hours of data but remain only partially annotated and thus underutilized, in part due to the prohibitive costs of annotating at this scale. In this work, we develop a pseudo-annotation pipeline that takes signed video and English as input and outputs a ranked set of likely annotations, including time intervals, for glosses, fingerspelled words, and sign classifiers. Our pipeline uses sparse predictions from our fingerspelling recognizer and isolated sign recognizer (ISR), along with a K-Shot LLM approach, to estimate these annotations. In service of this pipeline, we establish simple yet effective baseline fingerspelling and ISR models, achieving state-of-the-art on FSBoard (6.7% CER) and on ASL Citizen datasets (74% top-1 accuracy). To validate and provide a gold-standard benchmark, a professional interpreter annotated nearly 500 videos from ASL STEM Wiki with sequence-level gloss labels containing glosses, classifiers, and fingerspelling signs. These human annotations and over 300 hours of pseudo-annotations are being released in supplemental material.

  PublisherOA PDF

• Interface Support for Evaluating Disability Bias in AI-Generated Images

  2026-04-13 · 1 citations

  articleOpen accessSenior author

  Generative text-to-image (T2I) models often output images that have stereotypes of people with disabilities. One possibility to mitigate the risk of these biases is to intervene at the user level, supporting T2I users themselves in being able to identify biases and act accordingly. To understand how to design such support and its potential effectiveness, we implemented two interventions: (1) an education module to inform users of disability stereotypes in T2I images and (2) AI-generated feedback about potential stereotypes in a given image. We evaluated these options alone and in combination through a controlled experiment (N = 103) and a qualitative study (N = 10). Our results demonstrate that interface-based interventions can help users identify stereotypes, but that people do not always desire to avoid stereotypes. Participants wanted image subjects to “look” disabled, which sometimes inadvertently perpetuated stereotypes. Our results indicate clear ways for T2I interfaces to support users in prompting for and assessing images.

  PublisherDOI

• SceneScout: Towards AI-Driven Access to Street Level Imagery for Blind Users

  2026-04-13 · 1 citations

  articleOpen access

  People who are blind or have low-vision (BLV) may hesitate to travel independently in unfamiliar environments due to uncertainty about the physical landscape. While most tools focus on in-situ navigation assistance, those supporting pre-travel assistance typically provide information about only landmarks and turn-by-turn instructions, lacking detailed visual context. Street level imagery, which contains rich visual information and has the potential to reveal numerous environmental details, remains inaccessible to BLV people. In this work, we present SceneScout, a multimodal large language model (MLLM)-driven prototype that enables accessible interactions with street level imagery. SceneScout supports two modes: (1) Route Preview, enabling users to familiarize themselves with visual details along a route, and (2) Virtual Exploration, enabling free, user-driven movement within street level imagery. Our user study (N = 10) demonstrates that SceneScout helps BLV users uncover visual information otherwise unavailable through existing means. An initial analysis of AI-generated descriptions suggests that the majority are accurate and describe stable visual elements even in older imagery, though occasional subtle and plausible errors make them difficult to verify without sight. We discuss future opportunities and challenges of street level imagery-based navigation experiences.

  PublisherDOI

• Unseen City Canvases: Exploring Blind and Low Vision People's Perspectives on Urban and Public Art Accessibility

  arXiv (Cornell University) · 2026-03-27

  preprintOpen accessSenior author

  Public art can hold cultural, social, political, and aesthetic significance, enriching urban environments and promoting well-being. However, a majority of urban art is inaccessible to blind and low vision (BLV) people. Most art access research has focused on private and curated settings (e.g., museums, galleries) and most urban access work has centered on outdoor navigation, leaving urban and public art accessibility largely understudied. We conducted semi-structured interviews with 16 BLV participants, using design probes featuring AI-generated descriptions and real-time AI interactions to investigate preferences for both discovering and engaging with urban art. We found that BLV people valued spontaneous art exploration, multisensory (e.g., tactile, auditory, olfactory) engagement, and detailed descriptions of culturally significant artwork. Participants also highlighted challenges distinct to urban art contexts: safety took precedence over art exploration, multisensory access measures could be disruptive to others in the public space, and inaccurate AI descriptions could lead to cultural erasure. Our contributions include empirical insights on BLV preferences for urban art discovery and engagement, seven design dimensions for public art access solutions, and implications for expanding HCI urban accessibility research beyond navigation.

  PublisherDOI

• Unseen City Canvases: Exploring Blind and Low Vision People's Perspectives on Urban and Public Art Accessibility

  arXiv (Cornell University) · 2026-03-27

  articleOpen accessSenior author

  Public art can hold cultural, social, political, and aesthetic significance, enriching urban environments and promoting well-being. However, a majority of urban art is inaccessible to blind and low vision (BLV) people. Most art access research has focused on private and curated settings (e.g., museums, galleries) and most urban access work has centered on outdoor navigation, leaving urban and public art accessibility largely understudied. We conducted semi-structured interviews with 16 BLV participants, using design probes featuring AI-generated descriptions and real-time AI interactions to investigate preferences for both discovering and engaging with urban art. We found that BLV people valued spontaneous art exploration, multisensory (e.g., tactile, auditory, olfactory) engagement, and detailed descriptions of culturally significant artwork. Participants also highlighted challenges distinct to urban art contexts: safety took precedence over art exploration, multisensory access measures could be disruptive to others in the public space, and inaccurate AI descriptions could lead to cultural erasure. Our contributions include empirical insights on BLV preferences for urban art discovery and engagement, seven design dimensions for public art access solutions, and implications for expanding HCI urban accessibility research beyond navigation.

  PublisherOA PDF

• SceneScout: Towards AI Agent-driven Access to Street View Imagery for Blind Users

  ArXiv.org · 2025-04-12

  preprintOpen access

  People who are blind or have low vision (BLV) may hesitate to travel independently in unfamiliar environments due to uncertainty about the physical landscape. While most tools focus on in-situ navigation, those exploring pre-travel assistance typically provide only landmarks and turn-by-turn instructions, lacking detailed visual context. Street view imagery, which contains rich visual information and has the potential to reveal numerous environmental details, remains inaccessible to BLV people. In this work, we introduce SceneScout, a multimodal large language model (MLLM)-driven AI agent that enables accessible interactions with street view imagery. SceneScout supports two modes: (1) Route Preview, enabling users to familiarize themselves with visual details along a route, and (2) Virtual Exploration, enabling free movement within street view imagery. Our user study (N=10) demonstrates that SceneScout helps BLV users uncover visual information otherwise unavailable through existing means. A technical evaluation shows that most descriptions are accurate (72%) and describe stable visual elements (95%) even in older imagery, though occasional subtle and plausible errors make them difficult to verify without sight. We discuss future opportunities and challenges of using street view imagery to enhance navigation experiences.

  PublisherOA PDFDOI

• VizXpress: Towards Expressive Visual Content by Blind Creators Through AI Support

  2025-10-22 · 1 citations

  articleSenior author
  PublisherDOI

• Visual Privacy Management with Generative AI for Blind and Low-Vision People

  ArXiv.org · 2025-06-30

  preprintOpen access

  Blind and low vision (BLV) individuals use Generative AI (GenAI) tools to interpret and manage visual content in their daily lives. While such tools can enhance the accessibility of visual content and so enable greater user independence, they also introduce complex challenges around visual privacy. In this paper, we investigate the current practices and future design preferences of blind and low vision individuals through an interview study with 21 participants. Our findings reveal a range of current practices with GenAI that balance privacy, efficiency, and emotional agency, with users accounting for privacy risks across six key scenarios, such as self-presentation, indoor/outdoor spatial privacy, social sharing, and handling professional content. Our findings reveal design preferences, including on-device processing, zero-retention guarantees, sensitive content redaction, privacy-aware appearance indicators, and multimodal tactile mirrored interaction methods. We conclude with actionable design recommendations to support user-centered visual privacy through GenAI, expanding the notion of privacy and responsible handling of others data.

  PublisherOA PDFDOI

Recent grants

• CAREER: Scaling Up Mobile Accessibility Through Touchscreen Personalization

  NSF · $348k · 2017–2022

• CHS: Medium: Collaborative Research: Wearable Sound Sensing and Feedback Techniques for Persons who are Deaf or Hard of Hearing

  NSF · $916k · 2018–2024

• CAREER: Scaling Up Mobile Accessibility Through Touchscreen Personalization

  NSF · $436k · 2014–2018

Frequent coauthors

• Jon E. Froehlich

  University of Washington

  72 shared

• Jordan Boyd‐Graber

  37 shared

• Alison Smith

  24 shared

• Kevin Seppi

  22 shared

• Dhruv Jain

  21 shared

• Uran Oh

  Ewha Womans University

  20 shared

• Lee Stearns

  University of Maryland, College Park

  18 shared

• Varun Kumar

  Mahatma Gandhi Kashi Vidyapith

  17 shared

Awards & honors

• NSF CAREER Award