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

AI technology offers opportunities to expand virtual and physical access for people with disabilities. However, an important part of bringing these opportunities to fruition is ensuring that upcoming AI technology works well for people with a wide range of abilities. In this paper, we identify the lack of data from disabled populations as one of the challenges to training and benchmarking fair and inclusive AI systems. As a potential solution, we envision an online infrastructure that can enable large-scale, remote data contributions from disability communities. We investigate the motivations, concerns, and challenges that people with disabilities might experience when asked to collect and upload various forms of AI-relevant data through a semi-structured interview and an online survey that simulated a data contribution process by collecting example data files through an online portal. Based on our findings, we outline design guidelines for developers creating online infrastructures for gathering data from people with disabilities.

[1]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[2]  Laura A. Dabbish,et al.  Designing games with a purpose , 2008, CACM.

[3]  Paola Salomoni,et al.  On Combining Crowdsourcing, Sensing and Open Data for an Accessible Smart City , 2014, 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies.

[4]  Laurens van der Maaten,et al.  Does Object Recognition Work for Everyone? , 2019, CVPR Workshops.

[5]  Kimmo Kärkkäinen,et al.  FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age , 2019, ArXiv.

[6]  Meredith Ringel Morris,et al.  Toward fairness in AI for people with disabilities SBG@a research roadmap , 2019, ACM SIGACCESS Access. Comput..

[7]  Chi Lin,et al.  VizWiz-Priv: A Dataset for Recognizing the Presence and Purpose of Private Visual Information in Images Taken by Blind People , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Y. Benkler.  Don’t let industry write the rules for AI , 2019, Nature.

[9]  Shari Trewin,et al.  AI Fairness for People with Disabilities: Point of View , 2018, ArXiv.

[10]  Cynthia L. Bennett,et al.  What is the point of fairness? , 2020, ACM SIGACCESS Access. Comput..

[11]  Inioluwa Deborah Raji,et al.  Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing , 2020, AIES.

[12]  Nils J. Nilsson,et al.  The Quest for Artificial Intelligence , 2009 .

[13]  Lorrie Faith Cranor,et al.  Are your participants gaming the system?: screening mechanical turk workers , 2010, CHI.

[14]  S. Merriam.  Qualitative research in practice : examples for discussion and analysis , 2002 .

[15]  Oscar Koller,et al.  Exploring Collection of Sign Language Datasets: Privacy, Participation, and Model Performance , 2020, ASSETS.

[16]  A. Valeras.  “We don’t have a box”: Understanding Hidden Disability Identity Utilizing Narrative Research Methodology , 2010 .

[17]  Leah Findlater,et al.  Fairness issues in AI systems that augment sensory abilities , 2019, ACM SIGACCESS Access. Comput..

[18]  M. Mills,et al.  Disability, Bias, and AI , 2019 .

[19]  Meredith Ringel Morris,et al.  Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective , 2019, ASSETS.

[20]  Parag C. Pendharkar,et al.  A field study of the impact of gender and user's technical experience on the performance of voice-activated medical tracking application , 2004, Int. J. Hum. Comput. Stud..

[21]  Laura A. Dabbish,et al.  Labeling images with a computer game , 2004, AAAI Spring Symposium: Knowledge Collection from Volunteer Contributors.

[22]  White Guy.  Facial Recognition Is Accurate, if You’re a White Guy , 2018 .

[23]  Karen Nakamura,et al.  My Algorithms Have Determined You're Not Human: AI-ML, Reverse Turing-Tests, and the Disability Experience , 2019, ASSETS.

[24]  Jiebo Luo,et al.  VizWiz Grand Challenge: Answering Visual Questions from Blind People , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[25]  Rob Miller,et al.  VizWiz: nearly real-time answers to visual questions , 2010, UIST.

[26]  Meredith Ringel Morris,et al.  AI and Accessibility: A Discussion of Ethical Considerations , 2020 .

[27]  K. Dautenhahn,et al.  Comparing human robot interaction scenarios using live and video based methods: towards a novel methodological approach , 2006, 9th IEEE International Workshop on Advanced Motion Control, 2006..

[28]  Rachael Tatman,et al.  Gender and Dialect Bias in YouTube’s Automatic Captions , 2017, EthNLP@EACL.

[29]  I. van Hoyweghen,et al.  Science by, with and for citizens: rethinking ‘citizen science’ after the 2011 Fukushima disaster , 2020, Palgrave Communications.

[30]  Peter Buxmann,et al.  Envy on Facebook: A Hidden Threat to Users' Life Satisfaction? , 2013, Wirtschaftsinformatik.

[31]  Nils J. Nilsson,et al.  The Quest For Artificial Intelligence: A History Of Ideas And Achievements , 2009 .

[32]  Timnit Gebru,et al.  Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification , 2018, FAT.

[33]  Meredith Ringel Morris,et al.  Accessible Crowdwork?: Understanding the Value in and Challenge of Microtask Employment for People with Disabilities , 2015, CSCW.

[34]  Emily Denton,et al.  Unintended machine learning biases as social barriers for persons with disabilitiess , 2020, ACM SIGACCESS Access. Comput..

[35]  Matthew J. Jorgensen,et al.  An empirical study of moral intuitions: Toward an evolutionary ethics. , 1993 .

[36]  John Zimmerman,et al.  Swarthmore College , 2012 .

[37]  S. Merriam.  Merriam, Sharan B., ed., Qualitative Research in Practice: Examples for Discussion and Analysis. San Francisco: Jossey-Bass, 2002. , 2002 .

[38]  Fred Sampson.  Sense and accessibility , 2007, INTR.

[39]  Erin Brady,et al.  Interdependence as a Frame for Assistive Technology Research and Design , 2018, ASSETS.

[40]  Stuart MacFarlane,et al.  Children are ready for speech technology-but is the technology ready for them ? , 2004 .