publications

2025

1. 

   Exploring Reduced Feature Sets for American Sign Language Dictionaries

   Ben Kosa, Aashaka Desai, Alex X Lu, and 2 more authors

   In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, , 2025

   Abs DOI Bib PDF

   There is currently no easy way to look up signs in sign language. Feature-based dictionaries help overcome this challenge by enabling users to look up a sign by inputting descriptive visual features, such as handshape and movement. However, feature-based dictionaries are typically cumbersome, including large numbers of complex features that the user must sort through. In this work, we explore simplifying the set of features used in feature-based American Sign Language (ASL) dictionaries. We present two studies: 1) a simulation study focused on lookup accuracy for various reduced feature sets, and 2) a user study focused on understanding human preferences between feature sets. Our results suggest that it is possible to dramatically reduce the number of features needed to search for signs without significantly impacting the accuracy of search results, and that smaller feature sets can improve the user experience in some cases.

   @inproceedings{10.1145/3706598.3714118,
     author = {Kosa, Ben and Desai, Aashaka and Lu, Alex X and Ladner, Richard E. and Bragg, Danielle},
     title = {Exploring Reduced Feature Sets for American Sign Language Dictionaries},
     year = {2025},
     isbn = {9798400713941},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3706598.3714118},
     doi = {10.1145/3706598.3714118},
     booktitle = {Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems},
     articleno = {797},
     numpages = {14},
     keywords = {American Sign Language (ASL), Dictionary, Search, Education},
     location = {
     },
     series = {CHI '25},
   }

2. 

   Autoethnographic Insights from Neurodivergent GAI “Power Users”

   Kate Glazko, JunHyeok Cha, Aaleyah Lewis, and 5 more authors

   In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, , 2025

   Abs DOI Bib PDF

   Generative AI (AI) has become ubiquitous in both daily and professional life, with emerging research demonstrating its potential as a tool for accessibility. Neurodivergent people, often left out by existing accessibility technologies, develop their own ways of navigating normative expectations. GAI offers new opportunities for access, but it is important to understand how neurodivergent “power users”—successful early adopters—engage with it and the challenges they face. Further, we must understand how marginalization and intersectional identities influence their interactions with GAI. Our autoethnography, enhanced by privacy-preserving GAI-based diaries and interviews, reveals the intricacies of using GAI to navigate normative environments and expectations. Our findings demonstrate how GAI can both support and complicate tasks like code-switching, emotional regulation, and accessing information. We show that GAI can help neurodivergent users to reclaim their agency in systems that diminish their autonomy and self-determination. However, challenges such as balancing authentic self-expression with societal conformity, alongside other risks, create barriers to realizing GAI’s full potential for accessibility.

   @inproceedings{10.1145/3706598.3713670,
     author = {Glazko, Kate and Cha, JunHyeok and Lewis, Aaleyah and Kosa, Ben and Wimer, Brianna L and Zheng, Andrew and Zheng, Yiwei and Mankoff, Jennifer},
     title = {Autoethnographic Insights from Neurodivergent GAI “Power Users”},
     year = {2025},
     isbn = {9798400713941},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3706598.3713670},
     doi = {10.1145/3706598.3713670},
     booktitle = {Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems},
     articleno = {274},
     numpages = {19},
     keywords = {auto-ethnography; generative artificial intelligence; accessibility; neurodivergent people; intersectionality; stigma},
     location = {
     },
     series = {CHI '25},
   }

2024

1. 

   Identifying and Improving Disability Bias in GPT-Based Resume Screening

   Kate Glazko, Yusuf Mohammed, Ben Kosa, and 2 more authors

   In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, Rio de Janeiro, Brazil, 2024

   Abs DOI Bib PDF

   As Generative AI rises in adoption, its use has expanded to include domains such as hiring and recruiting. However, without examining the potential of bias, this may negatively impact marginalized populations, including people with disabilities. To address this important concern, we present a resume audit study, in which we ask ChatGPT (specifically, GPT-4) to rank a resume against the same resume enhanced with an additional leadership award, scholarship, panel presentation, and membership that are disability-related. We find that GPT-4 exhibits prejudice towards these enhanced CVs. Further, we show that this prejudice can be quantifiably reduced by training a custom GPTs on principles of DEI and disability justice. Our study also includes a unique qualitative analysis of the types of direct and indirect ableism GPT-4 uses to justify its biased decisions and suggest directions for additional bias mitigation work. Additionally, since these justifications are presumably drawn from training data containing real-world biased statements made by humans, our analysis suggests additional avenues for understanding and addressing human bias.

   @inproceedings{10.1145/3630106.3658933,
     author = {Glazko, Kate and Mohammed, Yusuf and Kosa, Ben and Potluri, Venkatesh and Mankoff, Jennifer},
     title = {Identifying and Improving Disability Bias in GPT-Based Resume Screening},
     year = {2024},
     isbn = {9798400704505},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3630106.3658933},
     doi = {10.1145/3630106.3658933},
     booktitle = {Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency},
     pages = {687–700},
     numpages = {14},
     keywords = {Ableism, Bias, GPT, Resume Audit},
     location = {Rio de Janeiro, Brazil},
     series = {FAccT '24},
   }

2. 

   Accessible Informed Consent Process in Interactive ASL Apps

   Hannah Benjamin, Natnail Tolossa, Michaela Brandt, and 3 more authors

   The Journal on Technology and Persons with Disabilities, 2024

   Bib PDF

   @article{benjamin2024accessible,
     title = {Accessible Informed Consent Process in Interactive ASL Apps},
     author = {Benjamin, Hannah and Tolossa, Natnail and Brandt, Michaela and Kosa, Ben and Kushalnagar, Poorna and Kushalnagar, Raja},
     journal = {The Journal on Technology and Persons with Disabilities},
     pages = {162},
     year = {2024},
   }

2023

1. 

   ASL Consent in the Digital Informed Consent Process

   Ben Kosa, Ai Minakawa, Patrick Boudreault, and 3 more authors

   The Journal on Technology and Persons with Disabilities, 2023

   Bib PDF

   @article{kosa2023asl,
     title = {ASL Consent in the Digital Informed Consent Process},
     author = {Kosa, Ben and Minakawa, Ai and Boudreault, Patrick and Vogler, Christian and Kushalnagar, Poorna and Kushalnagar, Raja},
     year = {2023},
     journal = {The Journal on Technology and Persons with Disabilities},
   }