Cited by 189Ethics and Social Impacts of AIDigital Accessibility for DisabilitiesAssistive Technology in Communication and Mobility

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Toward fairness in AI for people with disabilities SBG@a research roadmap

Guo, Anhong, Kamar, Ece, Vaughan, Jennifer Wortman, Wallach, Hanna, Morris, Meredith Ringel·2020·ACM SIGACCESS Accessibility and Computing

DOI 10.1145/3386296.3386298

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Abstract

AI technologies have the potential to dramatically impact the lives of people with disabilities (PWD). Indeed, improving the lives of PWD is a motivator for many state-of-the-art AI systems, such as automated speech recognition tools that can caption videos for people who are deaf and hard of hearing, or language prediction algorithms that can augment communication for people with speech or cognitive disabilities. However, widely deployed AI systems may not work properly for PWD, or worse, may actively discriminate against them. These considerations regarding fairness in AI for PWD have thus far received little attention. In this position paper, we identify potential areas of concern regarding how several AI technology categories may impact particular disability constituencies if care is not taken in their design, development, and testing. We intend for this risk assessment of how various classes of AI might interact with various classes of disability to provide a roadmap for future research that is needed to gather data, test these hypotheses, and build more inclusive algorithms.

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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1993.341010 · Cited by 14,475
Equality of Opportunity in Supervised Learning
Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536
Certifying and Removing Disparate Impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783311 · Cited by 1,827
The social model of disability: thirty years on
Oliver, M. (2013) · Disability & Society · doi:10.1080/09687599.2013.818773 · Cited by 1,648
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016) · arXiv · doi:10.48550/arxiv.1607.06520 · Cited by 1,615
Silhouette analysis-based gait recognition for human identification
Wang, L., Tan, T., Ning, H., & Hu, W. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1251144 · Cited by 1,205
Using Psychological Artificial Intelligence (Tess) to Relieve Symptoms of Depression and Anxiety: Randomized Controlled Trial (Preprint)
Fulmer, R., Joerin, A., Gentile, B., Lakerink, L., & Rauws, M. (2018) · doi:10.2196/preprints.9782 · Cited by 715
Fairness-Aware Classifier with Prejudice Remover Regularizer
Kamishima, T., Akaho, S., Asoh, H., & Sakuma, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33486-3_3 · Cited by 694
An overview of automatic speaker diarization systems
Tranter, S. E., & Reynolds, D. A. (2006) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2006.878256 · Cited by 605

Show all 36 references

Research works printed but not linked: 22

Julia Angwin, Jeff Larson, Surya Mattu, Lauren Kirchner, and ProPublica. 2016. Machine Bias: There's software used across the country to predict future criminals. And it's biased against blacks. (2016). Retrieved July 3, 2019 from https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
Apple Inc. 2019c. macOS Catalina - Introducing Voice Control. Your all-access to all devices. (2019). Retrieved July 3, 2019 from https://www.apple.com/macos/catalina-preview/#accessibility
Jeffrey P. Bigham and Patrick Carrington. 2018. Learning from the Front: People with Disabilities as Early Adopters of AI. In HCIC 2018.
Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on Fairness, Accountability and Transparency. 77--91.
Min Chu, Yong Zhao, and Sheng Zhao. 2010. Providing personalized voice font for text-to-speech applications. (April 6 2010). US Patent 7,693,719.
Duolingo. 2019. Say hello to the Bots. The most advanced way to learn a language. (2019). Retrieved July 3, 2019 from http://bots.duolingo.com
John Greer Elias, Wayne Carl Westerman, and Myra Mary Haggerty. 2010. Multi-touch gesture dictionary. (Nov. 23 2010). US Patent 7,840,912.
Linda G Gottermeier and S Kushalnagar Raja. 2016. User Evaluation of Automatic Speech Recognition Systems for Deaf-Hearing Interactions at School and Work. Audiology Today 28, 2 (2016), 20--34.
Grammarly Inc. 2019. Great Writing, Simplified. (2019). Retrieved July 3, 2019 from https://www.grammarly.com
HireVue. 2019. HireVue - Hiring Intelligence | Assessment & Video Interview Software. (2019). Retrieved July 3, 2019 from https://www.hirevue.com
LookTel. 2019. LookTel Money Reader. (2019). Retrieved July 3, 2019 from http://www.looktel.com/moneyreader
Microsoft. 2019a. Azure Kinect DK - BODY TRACKING SDK. (2019). Retrieved July 3, 2019 from https://azure.microsoft.com/en-us/services/kinect-dk/
Microsoft. 2019b. Custom Voice. (2019). Retrieved July 3, 2019 from https://speech.microsoft.com/customvoice
Microsoft. 2019d. Immersive Reader - An AI Service that helps users read and comprehend text. (2019). Retrieved July 3, 2019 from https://azure.microsoft.com/en-us/services/cognitive-services/immersive-reader/
Microsoft. 2019e. Seeing AI. (2019). Retrieved July 3, 2019 from https://www.microsoft.com/en-us/ai/seeing-ai
Microsoft. 2019f. Windows Hello: Discover facial recognition on Windows 10. (2019). Retrieved July 3, 2019 from https://www.microsoft.com/en-us/windows/windows-hello
Motion Analysis. 2019. Sports Biomechanics. (2019). Retrieved July 3, 2019 from https://motionanalysis.com/industry/sports-biomechanics/
Antony Nicol, Chris Casey, and Stuart MacFarlane. 2002. Children are ready for speech technology-but is the technology ready for them. Interaction Design and Children, Eindhoven, The Netherlands (2002).
OrCam. 2019. OrCam MyEye 2 - For the Blind and Visually Impaired. (2019). Retrieved July 3, 2019 from https://www.orcam.com/en/myeye2/
KNFB Reader. 2018. KNFB Reader gives you easy access to print and files, anytime, anywhere. (2018). Retrieved July 3, 2019 from https://knfbreader.com
TopResume. 2019. Get to Know the 5 Most Popular Pre-Employment Personality Tests. (2019). Retrieved July 3, 2019 from https://www.topresume.com/career-advice/how-to-pass-the-pre-employment-personality-test
Shari Trewin. 2018. AI Fairness for People with Disabilities: Point of View. arXiv preprint arXiv:1811.10670 (2018). · arXiv

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Authors: Guo, A., Kamar, E., Vaughan, J. W., Wallach, H., & Morris, M. R.
Affiliations: Microsoft (United States) (US)
ORCID: Guo, Anhong
Vaughan, Jennifer Wortman
Wallach, Hanna
Morris, Meredith Ringel
Venue: ACM SIGACCESS Accessibility and Computing
Publisher: Association for Computing Machinery (ACM)
Published: 2020-03-02
Issue: 125
Pages: 1-1
ISSN: 1558-1187 (ISSN-L)
1558-2337
DOI: 10.1145/3386296.3386298
OpenAlex: W3009306974
Cited by: 128
References: 36
Citations by year: 2026: 7 · 2025: 23 · 2024: 26 · 2023: 34 · 2022: 19 · 2021: 15 · 2020: 3
FWCI: 24.19
Type: journal article
Language: en
Open access: closed
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: ACM SIGACCESS Accessibility and Computing (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
Keywords: algorithmic fairness, people with disabilities, algorithmic discrimination, accessibility, speech recognition, deaf and hard of hearing, assistive technology, research roadmap

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