‘Hacking’ Assistive Technology: Creating and Customizing AI Tools for Personal Accessibility

Existing AI assistive applications for blind people tend to be designed for common use cases in order to be broadly applicable, for instance, reading printed text or identifying objects. Yet, individuals have unique skills, goals, and contexts, leading to a long-tail of tasks that one-size-fits-all assistive technology cannot address well. These gaps point to limitations in the current design process for assistive technologies: solutions are designed for broad groups of people, and participants are involved in limited stages of the design process (typically formative studies, short design check-ins, and evaluations). In this talk, I will present my research applying a Do-It-Yourself (DIY) approach to assistive technology, enabling people with disabilities to create AI-powered tools for their unique needs. In doing so, my approach shifts agency from researchers and developers to people with disabilities. I will first present my qualitative research on current technology gaps and DIY practices among blind people, showing how people with disabilities often already spend a high degree of effort customizing and creating workaround ‘hacks’ to make technology work for them. I will then present two tools aimed at harnessing and better supporting these DIY efforts, by enabling people to create and modify assistive technologies for themselves. Overall, my research aims to promote the democratization of AI technology creation, and support blind people in having greater control over AI-based technologies in their lives.

About our speaker
Jaylin Herskovitz joined the Department of Computer Science at Tufts University as an Assistant Professor in January 2026. Her research focuses on accessibility and AI-based assistive technologies, DIY technology, and AR/VR. Her work combines qualitative methods with technical system building, and she works with people with disabilities to design, develop, and evaluate AI-based assistive technology. Her work has been published in top-tier HCI conferences, including CHI, UIST, and ASSETS. Jaylin holds a Ph.D. in Computer Science from the University of Michigan, where she was advised by Anhong Guo. During her Ph.D., Jaylin was supported by an NSF Graduate Research Fellowship and completed internships with Apple AI/ML Research and Microsoft Research.

Naviagation: Enter the building through this gate and take left.

Nearest T station is Ruggles on Orange line and Northeastern University on Green line

‘Hacking’ Assistive Technology: Creating and Customizing AI Tools for Personal Accessibility

Existing AI assistive applications for blind people tend to be designed for common use cases in order to be broadly applicable, for instance, reading printed text or identifying objects. Yet, individuals have unique skills, goals, and contexts, leading to a long-tail of tasks that one-size-fits-all assistive technology cannot address well. These gaps point to limitations in the current design process for assistive technologies: solutions are designed for broad groups of people, and participants are involved in limited stages of the design process (typically formative studies, short design check-ins, and evaluations). In this talk, I will present my research applying a Do-It-Yourself (DIY) approach to assistive technology, enabling people with disabilities to create AI-powered tools for their unique needs. In doing so, my approach shifts agency from researchers and developers to people with disabilities. I will first present my qualitative research on current technology gaps and DIY practices among blind people, showing how people with disabilities often already spend a high degree of effort customizing and creating workaround ‘hacks’ to make technology work for them. I will then present two tools aimed at harnessing and better supporting these DIY efforts, by enabling people to create and modify assistive technologies for themselves. Overall, my research aims to promote the democratization of AI technology creation, and support blind people in having greater control over AI-based technologies in their lives.

About our speaker
Jaylin Herskovitz joined the Department of Computer Science at Tufts University as an Assistant Professor in January 2026. Her research focuses on accessibility and AI-based assistive technologies, DIY technology, and AR/VR. Her work combines qualitative methods with technical system building, and she works with people with disabilities to design, develop, and evaluate AI-based assistive technology. Her work has been published in top-tier HCI conferences, including CHI, UIST, and ASSETS. Jaylin holds a Ph.D. in Computer Science from the University of Michigan, where she was advised by Anhong Guo. During her Ph.D., Jaylin was supported by an NSF Graduate Research Fellowship and completed internships with Apple AI/ML Research and Microsoft Research.

Naviagation: Enter the building through this gate and take left.

Nearest T station is Ruggles on Orange line and Northeastern University on Green line

No AI about us, without us:  Engaging Disability Communities in Meaningful Data Work to Improve AI Models

AI image generation is being rapidly adopted, despite the popular and academic literature demonstrating that its representation of people with disabilities, and other marginalized groups, is at best poor, and often offensive. As visual media shapes public perceptions and hence access to education and employment, disability communities are calling for more influence over their representation in these models. In this talk, I will discuss the design and evaluation of the Community Library Creator, which enables communities to define ‘good’ representation and embody that in a diverse, structured dataset that can be used for pre-training large image generation models.

Speaker’s Bio
I am a Sr Principal Research Manager in Equitable AI at Microsoft Research Cambridge. I co-lead the Teachable AI Experience team (TAI X) which aims to innovate new human-AI interactions that bring us to a more inclusive society. I believe strongly that we must innovate the machine learning techniques that we use in conjunction with designing new types of experiences. I hold a PhD in Computer Science from University of Cambridge (opens in new tab) and an undergraduate degree in Ethnomusicology from Barnard College, Columbia University(opens in new tab). I currently live with my family in Massachusetts.

How to Get There
Public transportation: Take the Red Line Kendal Square. Upon arrival to 1 Memorial Drive, show your ID at the desk and take the elevators to Floor M.