Disability-First Design and Creation of A Dataset Showing Private Visual Information Collected With People Who Are Blind
Abstract
We present the design and creation of a disability-first dataset, “BIV-Priv,” which contains 728 images and 728 videos of 14 private categories captured by 26 blind participants to support downstream development of artificial intelligence (AI) models. While best practices in dataset creation typically attempt to eliminate private content, some applications require such content for model development. We describe our approach in creating this dataset with private content in an ethical way, including using props rather than participants’ own private objects and balancing multi-disciplinary perspectives (e.g., accessibility, privacy, computer vision) to meet the tangible metrics (e.g., diversity, category, amount of content) to support AI innovations. We observed challenges that our participants encountered during the data collection, including accessibility issues (e.g., understanding foreground vs. background object placement) and issues due to the sensitive nature of the content (e.g., discomfort in capturing some props such as condoms around family members).
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Prior efforts of disability-first datasets often focus on the data collection process.
“While prior efforts of disabilityfirst datasets often focus on the data collection process [36, 47, 63, 68, 88, 98], our study represents an important step in centering disabled people and their expertise throughout the AI development pipeline.”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [88]
Academic scholars and industry corporations have emphasized the pressing need for more disability-centered datasets.
“Although academic scholars and industry corporations have emphasized the pressing need for more disability-centered datasets [20, 38, 55, 79, 97, 111, 124]”
From "It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models (2026) · cites this paper as [111]
Cites
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“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI
Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., & Aroyo, L. M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445518 · Cited by 781 · see citing works
Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design
Yang, Q., Steinfeld, A., Rosé, C., & Zimmerman, J. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376301 · Cited by 622 · cited 1× · see citing works
“From the HCI literature related to dataset creation [37, 58, 75, 88], we found that collecting a dataset for AI innovation would work well when participants understand the study purpose and the tangible outcomes from it.”
BackgroundVizWiz Grand Challenge: Answering Visual Questions from Blind People
Gurari, D., Li, Q., Stangl, A. J., Guo, A., Lin, C., Grauman, K., Luo, J., & Bigham, J. P. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00380 · Cited by 621 · cited 6× · see citing works
“out-of-frame [29, 30] due to challenges associated with non-visual photography [10, 33].”
BackgroundMethodInfluentialSelf-citationDataSynthesizer: Privacy-Preserving Synthetic Datasets
Ping, H., Stoyanovich, J., & Howe, B. (2017) · Proceedings of the 29th International Conference on Scientific and Statistical Database Management · doi:10.1145/3085504.3091117 · Cited by 144 · see citing works
Helping visually impaired users properly aim a camera.
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Assessing Image Quality Issues for Real-World Problems
Chiu, T.-Y., Zhao, Y., & Gurari, D. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00370 · Cited by 63 · see citing works
Self-citationTheodorou, L., Massiceti, D., Zintgraf, L., Stumpf, S., Morrison, C., Cutrell, E., Harris, M. T., & Hofmann, K. (2021) · Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3441852.3471225 · Cited by 42 · see citing works
ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition
Massiceti, D., Zintgraf, L., Bronskill, J., Theodorou, L., Harris, M. T., Cutrell, E., Morrison, C., Hofmann, K., & Stumpf, S. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01064 · Cited by 33 · see citing works
A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication
Luccioni, A. S., Corry, F., Sridharan, H., Ananny, M., Schultz, J., & Crawford, K. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533086 · Cited by 22 · see citing works
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DIPA2: An Image Dataset with Cross-cultural Privacy Perception Annotations
Xu, A., Zhou, Z., Miyazaki, K., Yoshikawa, R., Hosio, S., & Yatani, K. (2023) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3631439 · Cited by 15 · see citing works
Tang, X., Li, J., & Wu, S. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790405 · Cited by 4 · see citing works
“In response, researchers have sourced data from disabled people to create accessibility datasets [26, 32, 36, 46, 63, 68, 88].”
InfluentialGarg, K., Tang, X., Heo, J., Morgan, D. R., Gergle, D., Sudderth, E. B., & Piper, A. M. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791309 · Cited by 3 · see citing works
“While training may help, BLV users still find it hard to properly orient objects or avoid unintentionally capturing private content in the background [111, 124].”
InfluentialCripping AI: Reimagining AI Through Lived Disability Experiences
Tang, X., Lin, T.-A., Li, J., & Wu, S. (2026) · Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3805689.3806744 · Cited by 2 · see citing works
“For instance, much research has focused on improving BLV people’s photo-taking skills [116, 173, 222, 252], as they often take “low-quality” images that are considered hard for AI models to process, i.e., blurry, poorly framed, or missing key objects [80, 81].”
- Authors
- Sharma, T., Stangl, A., Zhang, L., Tseng, Y.-Y., Xu, I., Findlater, L., Gurari, D., & Wang, Y.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- University of Washington (US)
- University of Colorado Denver (US)
- University of California System (US)
- University of Colorado Boulder (US)
- ORCID
- Venue
- Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2023-04-19
- Pages
- 1-15
- OpenAlex
- W4366547604
- Cited by
- 23
- References
- 11
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 4 · 2023: 2
- FWCI
- 5.76
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) (submittedVersion)
- Funders
- National Science Foundation (NSF) (#2126314 and #2028387.)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
- Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
- Keywords
- accessibility, privacy, computer vision, assistive technology
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