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

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 · see citing works

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 14,474 · see citing works

Equality of Opportunity in Supervised Learning

Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536 · see citing works

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 · see citing works

The social model of disability: thirty years on

Oliver, M. (2013) · Disability & Society · doi:10.1080/09687599.2013.818773 · Cited by 1,648 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 597 · see citing works

Unequal Representation and Gender Stereotypes in Image Search Results for Occupations

Kay, M., Matuszek, C., & Munson, S. A. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702520 · Cited by 509 · see citing works

Digits

Kim, D., Hilliges, O., Izadi, S., Butler, A., Chen, J., Oikonomidis, I., & Olivier, P. L. (2012) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2380116.2380139 · Cited by 428 · see citing works

Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective

Bragg, D., Koller, O., Bellard, M., Berke, L., Boudreault, P., Braffort, A., Caselli, N., Huenerfauth, M., Kacorri, H., Verhoef, T., et al. (2019) · Proceedings of the 21st International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3308561.3353774 · Cited by 422 · see citing works

A Reductions Approach to Fair ClassificationPreprint

Agarwal, A., Beygelzimer, A., Dudı́k, M., Langford, J. C., & Wallach, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.02453 · Cited by 364 · see citing works

Gender and Dialect Bias in YouTube’s Automatic Captions

Tatman, R. (2017) · Proceedings of the First ACL Workshop on Ethics in Natural Language Processing · doi:10.18653/v1/w17-1606 · Cited by 338 · see citing works

ViBand

Laput, G., Xiao, R., & Harrison, C. (2016) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2984511.2984582 · Cited by 268 · see citing works

Understanding Blind People's Experiences with Computer-Generated Captions of Social Media Images

MacLeod, H., Bennett, C. L., Morris, M. R., & Cutrell, E. (2017) · Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025814 · Cited by 200 · see citing works

Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration

Lakkaraju, H., Kamar, E. S., Caruana, R., & Horvitz, E. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10821 · Cited by 146 · see citing works

Head-Mounted Display Visualizations to Support Sound Awareness for the Deaf and Hard of Hearing

Jain, D., Findlater, L., Gilkeson, J., Holland, B., Duraiswami, R., Zotkin, D., Vogler, C., & Froehlich, J. E. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702393 · Cited by 123 · see citing works

Accessible Crowdwork?

Zyskowski, K., Morris, M. R., Bigham, J. P., Gray, M. L., & Kane, S. K. (2015) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2675133.2675158 · Cited by 116 · see citing works

Smart Touch

Mott, M. E., Vatavu, R.-D., Kane, S. K., & Wobbrock, J. O. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858390 · Cited by 76 · see citing works

Exploring Augmented Reality Approaches to Real-Time Captioning

Jain, D., Chinh, B., Findlater, L., Kushalnagar, R., & Froehlich, J. E. (2018) · ACM Conference Companion Publication on Designing Interactive Systems (DIS Companion) · doi:10.1145/3197391.3205404 · Cited by 64 · see citing works

Writing about accessibility

Hanson, V. L., Cavender, A. C., & Trewin, S. M. (2015) · interactions · doi:10.1145/2828432 · Cited by 57 · see citing works

A field study of the impact of gender and user's technical experience on the performance of voice-activated medical tracking application

Rodger, J. A., & Pendharkar, P. C. (2003) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2003.09.005 · Cited by 56 · see citing works

"At times avuncular and cantankerous, with the reflexes of a mongoose": Understanding Self-Expression through Augmentative and Alternative Communication Devices

Kane, S. K., Morris, M. R., Paradiso, A., & Campbell, J. (2017) · Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing · doi:10.1145/2998181.2998284 · Cited by 50 · see citing works

Understanding the Needs of Searchers with Dyslexia

Morris, M. R., Fourney, A., Ali, A. X., & Vonessen, L. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173609 · Cited by 46 · see citing works

Exploring voice user interfaces for seniors

Schlögl, S., Chollet, G., Garschall, M., Tscheligi, M., & Legouverneur, G. (2013) · Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments · doi:10.1145/2504335.2504391 · Cited by 44 · see citing works

VERSE: Bridging Screen Readers and Voice Assistants for Enhanced Eyes-Free Web Search

Vtyurina, A., Fourney, A., Morris, M. R., Findlater, L., & White, R. W. (2019) · Proceedings of the 21st International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3308561.3353773 · Cited by 42 · see citing works

StateLens: A Reverse Engineering Solution for Making Existing Dynamic Touchscreens Accessible

Guo, A., Kong, J., Rivera, M., Xu, F. F., & Bigham, J. P. (2019) · Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3332165.3347873 · Cited by 35 · see citing works

Cluster Touch

Mott, M. E., & Wobbrock, J. O. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300257 · Cited by 35 · see citing works

A Spellchecker for Dyslexia

Rello, L., Ballesteros, M., & Bigham, J. P. (2015) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/2700648.2809850 · Cited by 33 · see citing works

Towards More Robust Speech Interactions for Deaf and Hard of Hearing Users

Fok, R., Kaur, H., Palani, S., Mott, M. E., & Lasecki, W. S. (2018) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3234695.3236343 · Cited by 33 · see citing works

Exploring tilt for no-touch, wrist-only interactions on smartwatches

Guo, A., & Paek, T. (2016) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/2935334.2935345 · Cited by 32 · see citing works

HCI for people with cognitive disabilities

Lewis, C. (2005) · ACM SIGACCESS Accessibility and Computing · doi:10.1145/1102187.1102190 · Cited by 22 · see citing works

Understanding the Accessibility of Smartphone Photography for People with Motor Impairments

Mott, M. E., E, J. L., Bennett, C. L., Cutrell, E. B., & Morris, M. R. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174094 · Cited by 21 · see citing works

Voicesetting

Fiannaca, A. J., Paradiso, A., Campbell, J., & Morris, M. R. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173857 · Cited by 21 · see citing works

Algorithmic bias in education

Baker, R. S., & Hawn, A. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00285-9 · Cited by 993 · see citing works

Machine Learning in Mental Health: A Systematic Review of the HCI Literature to Support the Development of Effective and Implementable ML Systems

Thieme, A., Belgrave, D., & Doherty, G. (2020) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3398069 · Cited by 406 · see citing works

Inherent Tradeoffs in the Fair Determination of Risk Scores

Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603197 · Cited by 347 · see citing works

Disability, fairness, and algorithmic bias in AI recruitment

Tilmes, N. (2022) · Ethics and Information Technology · doi:10.1007/s10676-022-09633-2 · Cited by 169 · see citing works

Spatiotemporal data mining: a survey on challenges and open problems

Hamdi, A., Shaban, K. B., Erradi, A., Mohamed, A. M. S., Rumi, S. K., & Salim, F. D. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09994-y · Cited by 164 · see citing works

"I wouldn't say offensive but...": Disability-Centered Perspectives on Large Language Models

Gadiraju, V., Kane, S., Dev, S., Taylor, A., Wang, D., Denton, R., & Brewer, R. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3593989 · Cited by 155 · see citing works

Considerations for AI fairness for people with disabilities

Trewin, S., Basson, S., Muller, M., Branham, S., Treviranus, J., Gruen, D., Hebert, D., Lyckowski, N., & Manser, E. (2019) · AI Matters · doi:10.1145/3362077.3362086 · Cited by 143 · see citing works

"Person, Shoes, Tree. Is the Person Naked?" What People with Vision Impairments Want in Image Descriptions

Stangl, A., Morris, M. R., & Gurari, D. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376404 · Cited by 139 · see citing works

“It’s Complicated”: Negotiating Accessibility and (Mis)Representation in Image Descriptions of Race, Gender, and Disability

Bennett, C. L., Gleason, C., Scheuerman, M. K., Bigham, J. P., Guo, A., & To, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445498 · Cited by 128 · see citing works

Evaluating Inclusivity and Fairness of AI Recruitment Tools for Hiring People with Disabilities in the United Arab Emirates(UAE)

Iftikhar, A., Mohammad, S. I., Alqudah, M. A., Al‐Adwan, A. S., Vasudevan, A., Ali, I., & Farhan, M. (2024) · Data & Metadata · doi:10.56294/dm2025487 · Cited by 125 · see citing works

Artificial intelligence and machine learning in emergency medicine: a narrative review

Mueller, B., Kinoshita, T., Peebles, A., Graber, M. A., & Lee, S. (2022) · Acute Medicine & Surgery · doi:10.1002/ams2.740 · Cited by 118 · see citing works

AI and accessibility

Morris, M. R. (2020) · Communications of the ACM · doi:10.1145/3356727 · Cited by 114 · see citing works

Digital Design Marginalization: New Perspectives on Designing Inclusive Interfaces

Sin, J., Franz, R. L., Munteanu, C., & Neves, B. B. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445180 · Cited by 85 · see citing works

Tackling Algorithmic Disability Discrimination in the Hiring Process: An Ethical, Legal and Technical Analysis

Buyl, M., Cociancig, C., Frattone, C., & Roekens, N. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533169 · Cited by 85 · see citing works

Artificial intelligence in multimodal learning analytics: a systematic literature review

Mohammadi, M., Tajik, E., Martinez-Maldonado, R., Sadiq, S., Tomaszewski, W., & Khosravi, H. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100426 · Cited by 85 · see citing works

Going Beyond One-Size-Fits-All Image Descriptions to Satisfy the Information Wants of People Who are Blind or Have Low Vision

Stangl, A., Verma, N., Fleischmann, K. R., Morris, M. R., & Gurari, D. (2021) · Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3441852.3471233 · Cited by 81 · see citing works

Unlock Life with a Chat(GPT): Integrating Conversational AI with Large Language Models into Everyday Lives of Autistic Individuals

Choi, D., Lee, S., Kim, S.-I., Lee, K., Yoo, H. J., Lee, S., & Hong, H. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641989 · Cited by 79 · see citing works

A survey on neural networks for (cyber-) security and (cyber-) security of neural networks

Pawlicki, M., Kozik, R., & Choraś, M. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.06.002 · Cited by 78 · see citing works

AI-driven assistive technologies in inclusive education: benefits, challenges, and policy recommendations

Kooli, C., & CHAKRAOUI, R. (2025) · Sustainable Futures · doi:10.1016/j.sftr.2025.101042 · Cited by 81 · see citing works

Conceptualising fairness: three pillars for medical algorithms and health equity

Sikstrom, L., Maslej, M. M., Hui, K., Findlay, Z., Buchman, D. Z., & Hill, S. L. (2022) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100459 · Cited by 76 · see citing works

Scientism as illusio in HR algorithms: Towards a framework for algorithmic hygiene for bias proofing

Vassilopoulou, J., Kyriakidou, O., Özbilgin, M. F., & Groutsis, D. G. (2022) · Human Resource Management Journal · doi:10.1111/1748-8583.12430 · Cited by 75 · see citing works

Artificial intelligence and disability: too much promise, yet too little substance?

Smith, P., & Smith, L. (2021) · AI and Ethics · doi:10.1007/s43681-020-00004-5 · Cited by 72 · see citing works

Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review

Pierrès, O., Christen, M., Schmitt, F., & Darvishy, A. (2024) · IEEE Access · doi:10.1109/access.2024.3365368 · Cited by 72 · see citing works

Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices

Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603203 · Cited by 67 · see citing works

Artificial intelligence and labour market matching

Broecke, S. (2023) · OECD social employment and migration working papers · doi:10.1787/2b440821-en · Cited by 64 · see citing works

ImageExplorer: Multi-Layered Touch Exploration to Encourage Skepticism Towards Imperfect AI-Generated Image Captions

Lee, J., Herskovitz, J., Peng, Y.-H., & Guo, A. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501966 · Cited by 61 · see citing works

You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings

Talat, Z., Névéol, A., Biderman, S., Clinciu, M., Dey, M., Longpre, S., Luccioni, A. S., Masoud, M., Mitchell, M., Radev, D., et al. (2022) · doi:10.18653/v1/2022.bigscience-1.3 · Cited by 61 · see citing works

Cultural tourist and user experience with artificial intelligence: a holistic perspective from the Industry 5.0 approach

Orea-Giner, A., Mazón, A. I. M., Villacé-Molinero, T., & Moraleda, L. F. (2022) · Journal of Tourism Futures · doi:10.1108/jtf-04-2022-0115 · Cited by 54 · see citing works

Mitigating Bias in Algorithmic Employment Screening: Evaluating Claims and Practices

Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3408010 · Cited by 51 · see citing works

Designing an Online Infrastructure for Collecting AI Data From People With Disabilities

Park, J. S., Bragg, D., Kamar, E., & Morris, M. R. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445870 · Cited by 50 · see citing works

On Fairness and Calibration

Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603207 · Cited by 49 · see citing works

Exploring the role of generative AI in higher education: Semi-structured interviews with students with disabilities

Pierrès, O., Darvishy, A., & Christen, M. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13134-8 · Cited by 48 · see citing works

Understanding the Representation and Representativeness of Age in AI Data Sets

Park, J., Bernstein, M. S., Brewer, R. N., Kamar, E., & Morris, M. (2021) · AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462590 · Cited by 51 · see citing works

Hey ASR System! Why Aren’t You More Inclusive?

Ngueajio, M. K., & Washington, G. J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21707-4_30 · Cited by 43 · see citing works

ARELE-bot: Inclusive Learning of Spanish as a Foreign Language Through a Mobile App Integrating Augmented Reality and ChatGPT

Hajahmadi, S., Clementi, L., Jiménez‐López, M. D., & Marfia, G. (2024) · IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) · doi:10.1109/vrw62533.2024.00067 · Cited by 43 · see citing works

Disability-first Dataset Creation: Lessons from Constructing a Dataset for Teachable Object Recognition with Blind and Low Vision Data Collectors

Theodorou, 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

Data Representativeness in Accessibility Datasets: A Meta-Analysis

Kamikubo, R., Wang, L., Marte, C., Mahmood, A., & Kacorri, H. (2022) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3517428.3544826 · Cited by 41 · see citing works

Sense and Accessibility

Kane, S. K., Guo, A., & Morris, M. R. (2020) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3373625.3416990 · Cited by 36 · see citing works

Recruitment AI Has a Disability Problem: Anticipating and Mitigating Unfair Automated Hiring Decisions

Nugent, S. E., & Scott‐Parker, S. (2022) · International series on intelligent systems, control and automation: science and engineering · doi:10.1007/978-3-031-09823-9_6 · Cited by 34 · see citing works

Validation of portable in-clinic video-based gait analysis for prosthesis users

Cimorelli, A., Patel, A., Karakostas, T. E., & Cotton, R. J. (2024) · Scientific Reports · doi:10.1038/s41598-024-53217-7 · Cited by 34 · see citing works

Designing AACs for People with Aphasia Dining in Restaurants

Obiorah, M. G., Piper, A. M., & Horn, M. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445280 · Cited by 32 · see citing works

Sonic Technologies of a Queer Breakup

Kinnee, B., Rosner, D. K., & Desjardins, A. (2022) · Designing Interactive Systems Conference · doi:10.1145/3532106.3533542 · Cited by 30 · see citing works

Augmented Datasheets for Speech Datasets and Ethical Decision-Making

Papakyriakopoulos, O., Choi, A. S. G., Thong, W. E., Zhao, D., Andrews, J. T. A., Bourke, R., Xiang, A., & Koenecke, A. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594049 · Cited by 30 · see citing works

Intersectional Bias in Causal Language ModelsPreprint

Magee, L., Ghahremanlou, L., Soldatić, K., & Robertson, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.07691 · Cited by 30 · see citing works

Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education?

Holstein, K., & Doroudi, S. (2021) · arXiv · doi:10.48550/arxiv.2104.12920 · Cited by 29 · see citing works

Viewpoint: AI as Author – Bridging the Gap Between Machine Learning and Literary Theory

Van Heerden, I., & Bas, A. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12593 · Cited by 29 · see citing works

Enhancing Inclusivity: Exploring AI Applications for Diverse Learners

Hadinezhad, S., Garg, S., & Lindgren, R. (2024) · Postdigital science and education · doi:10.1007/978-3-031-64487-0_8 · Cited by 28 · see citing works

Empowering Student Learning Through Artificial Intelligence: A Bibliometric Analysis

Yun, G., Lee, K. M., & Choi, H. H. (2024) · Journal of Educational Computing Research · doi:10.1177/07356331241278636 · Cited by 26 · see citing works

Trustworthy Artificial Intelligence in Education: Pitfalls and PathwaysPreprint

Smuha, N. A. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3742421 · Cited by 25 · see citing works

The Cost of Ethical AI Development for AI Startups

Bessen, J., Impink, S. M., & Seamans, R. (2022) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3514094.3534195 · Cited by 22 · see citing works

Seeking information about assistive technology: Exploring current practices, challenges, and the need for smarter systems

Danemayer, J., Holloway, C., Cho, Y., Bianchi‐Berthouze, N., Singh, A., Bhot, W., Dixon, O., Grobelnik, M., & Shawe-Taylor, J. S. (2023) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2023.103078 · Cited by 22 · see citing works

A Systematic Review of Accessibility Techniques for Online Platforms: Current Trends and Challenges

Bercaru, V., & Popescu, N. (2024) · Applied Sciences · doi:10.3390/app142210337 · Cited by 21 · see citing works

Selection Problems in the Presence of Implicit Bias

Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603209 · Cited by 21 · see citing works

“It’s not wrong, but I’m quite disappointed”: Toward an Inclusive Algorithmic Experience for Content Creators with Disabilities

Choi, D., Lee, U., & Hong, H. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517574 · Cited by 21 · see citing works

Using AI to support people with disability in the labour market

Touzet, C. (2023) · OECD artificial intelligence papers · doi:10.1787/008b32b7-en · Cited by 21 · see citing works

The Externalities of Exploration and How Data Diversity Helps Exploitation

Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603208 · Cited by 20 · see citing works

IncluSet: A Data Surfacing Repository for Accessibility Datasets

Kacorri, H., Dwivedi, U., Amancherla, S., Jha, M., & Chanduka, R. (2020) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3373625.3418026 · Cited by 20 · see citing works

Contributing to Accessibility Datasets: Reflections on Sharing Study Data by Blind People

Kamikubo, R., Lee, K., & Kacorri, H. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581337 · Cited by 19 · see citing works

Recruitment AI has a Disability Problem: questions employers should be asking to ensure fairness in recruitmentPreprint

Nugent, S. E., Jackson, P., Scott‐Parker, S., Partridge, J., Raper, R., Bakalis, C., Shepherd, A., Mitra, A., Long, J., Maynard, K., et al. (2020) · doi:10.31235/osf.io/emwn5 · Cited by 19 · see citing works

How Could Equality and Data Protection Law Shape AI Fairness for People with Disabilities?

Binns, R., & Kirkham, R. (2021) · ACM Transactions on Accessible Computing · doi:10.1145/3473673 · Cited by 18 · see citing works

AI-enabled AT Framework: a principles-based approach to emerging assistive technology

Silvera‐Tawil, D., Higgins, L., Packer, K., Bayor, A. A., Walker, J. G., Li, J., Niven, P., Khanna, S., Byrnes, J., Bradford, D., et al. (2025) · Disability and Rehabilitation Assistive Technology · doi:10.1080/17483107.2025.2479838 · Cited by 16 · see citing works

Disability Discrimination in the Digital Realm: How the ICRPD Applies to Artificial Intelligence Decision-Making Processes and Helps in Determining the State of International Human Rights Law

Krupiy, T., & Scheinin, M. (2023) · Human Rights Law Review · doi:10.1093/hrlr/ngad019 · Cited by 16 · see citing works

Artificial Intelligence and Health Equity for People with Disabilities: An Integrated Framework for Disability-Inclusive AI Design

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AI Use in Criminal Matters as Permitted Under EU Law and as Needed to Safeguard the Essence of Fundamental Rights

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Into the Unknown: Exploration of Search Engines’ Responses to Users with Depression and Anxiety

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AI Must Be Anti-Ableist and Accessible

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Trust, Workload, and Performance in Human–Artificial Intelligence Partnering: The Role of Artificial Intelligence Attributes in Solving Classification Problems

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An Introduction to Algorithmic FairnessPreprint

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The ethical issues raised by the use of Artificial Intelligence products for the disabled: an analysis by two disabled people

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Therapy as an NLP Task: Comparing LLMs and Human Peers Behaviors in CBT Sessions

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Inclusive Recommender Systems: Addressing Needs of Users with ADHD

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Ethics surrounding artificial intelligence-powered assistive technologies used by people with visual and hearing impairments: a systematic review

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Heuristic Evaluation of AI-Powered Web Accessibility Assistants

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I Dream of Siri: Magic and Female Voice Assistants

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Analyzing Fairness of Computer Vision and Natural Language Processing Models

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Artificial intelligence and rights of people with disabilities: The power of algorithms

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Ethical Considerations in Affective Computing

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Experiences of college teachers with visual disability us-ing AT and AI based solutions in India: Benefits, Issues, Challenges and Prospects

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Culture Sensitivity and Information Access: A Qualitative Study among Ethnic Groups

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Strategically Using Applied Machine Learning for Accessibility Documentation in the Built Environment

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The effects of a mobile-based vocational skill building coaching technology intervention for people with cognitive disabilities: A pilot feasibility study

Heyn, P. C., Goldberg, A., McGrew, G., & Bodine, C. (2021) · Journal of Rehabilitation and Assistive Technologies Engineering · doi:10.1177/20556683211009731 · Cited by 5 · see citing works

Toward Fairness in AI for People with Disabilities: A Research RoadmapPreprint

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Considerations for AI fairness for people with disabilities

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Artificial intelligence fairness in the context of accessibility research on intelligent systems for people who are deaf or hard of hearing

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Contribution of artificial intelligence research to the development of inclusive applications for people with disabilities: A scoping review (2015–2025)

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Disability and algorithmic fairness in healthcare: a narrative review

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