Human-Centered Artificial Intelligence Lab

HCAIL at the the FAcct 2022 conference HCAIL group photo at FAcct’22 in June 2022!

Welcome to the Human-Centered Artificial Intelligence Lab (HCAIL)! We are a research group in the Department of Computer Science and Engineering at the University of Seoul. We enjoy working on the intersection of multiple domains: Human-centered AI, Digital Health, Social Computing, and Accessibility & Aging. We are pursing research in the combinations of the following directions:

Artificial Intelligence
Health Informatics
Human-Computer Interaction

Our mission is to advance AI research through design and engineering to support individuals with special needs.

Interested in joining HCAIL as a MS or PhD student?

We have an opening position for a fully-funded MS and PhD student starting Spring 2023. Visit [석사과정/박사과정 모집공고]. Send your resume to me so that we can discuss about how to join HCAIL a MS or PhD student. For Fall 2023, we aim to admit up to 2-3 new MS or PhD students.

HCAIL 연구실 자료 참고바랍니다: [슬라이드] [영상]

Interested in a research opportunity for undergraduate students?

Visit [학부연구생 인턴 모집공고] and send your resume to me if you are interested in a unique research intern opportunity. HCAIL is ready for maximizing your research potential during your internship.

news

May 2023	Our paper has been accepted for publication in the Heliyon journal.
Apr 2023	Welcome! Yunseo Moon joined HCAIL as a undergraduate research intern!
Mar 2023	Three papers were presented at IUI 2023 in Sydney, Australia.
Mar 2023	Congratulations! Hyunmin Lee was selected as a Gary Marsden Travel Awards recipient.
Mar 2023	Our paper was presented at AMIA 2023 Informatics Summit in Seattle, U.S.

selected publications

IUI

WATAA: Web Alternative Text Authoring Assistant for Improving Web Content Accessibility

Hyeonhak Jeong, Minki Chun, Hyunmin Lee, Seung Young Oh, and Hyunggu Jung

In Companion Proceedings of the 28th International Conference on Intelligent User Interfaces 2023

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Alternative (alt) text is essential for people with visual impairments to acquire information about image content through a screen reader. However, collecting images and creating alt text requires time and effort. To deal with this problem, automatically collecting images that have no alt text is essential to ensure that all image content in a web page has their alt text. Additionally, automatic alt text has limitations in accuracy and quality compared to human-created alt text despite the improvements in image recognition and natural language process technology. We present WATAA, a web alt text authoring assistant that collects images containing no alt text and suggests automatic alt text to help human alt text authors improve a web page’s accessibility.
IUI

PORDE: Explaining Data Poisoning Attacks Through Visual Analytics with Food Delivery App Reviews

Hyunmin Lee, Minki Chun, and Hyunggu Jung

In Companion Proceedings of the 28th International Conference on Intelligent User Interfaces 2023

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Artificial intelligence (AI) gives many benefits to our lives. However, biased AI models created by receiving data poisoning attacks may induce social problems. Therefore, developers must consider carefully whether the training data received a poison attack when developing an AI model. Data visualization is one of the methods to facilitate the analysis of the data required for checking if the training data received a poisoning attack. However, prior studies did not visualize real-world AI training data. Restaurant reviews in delivery apps are one of the cases of a poisoned dataset. Restaurants hold review events on delivery apps to encourage customers to write a positive review in return for certain rewards, thereby creating reviews with bias. In this study, we propose POisoned Real-world Data Explainer (PORDE) that explains data poisoning attacks through visual analytics with food delivery app reviews. The findings of this study suggest implications for securing safe training data and developing less biased AI models.
IUI

Toward Keyword Generation through Large Language Models

Wanhae Lee, Minki Chun, Hyeonhak Jeong, and Hyunggu Jung

In Companion Proceedings of the 28th International Conference on Intelligent User Interfaces 2023

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It is essential to understand research trends for researchers, decision-makers, and investors. One way to analyze research trends is to collect and analyze author-defined keywords in scientific papers. Unfortunately, while author-defined keywords are beneficial to researchers aiming to figure out the trends of their research fields, 45% of scientific papers in Microsoft Academic Graph did not contain their author-defined keywords. Additionally, six of the top seven AI conferences neither collect nor disclose keywords. This paper proposes a method for generating the keywords using Galactica, a pre-trained large language model published by Meta. We evaluate this method’s performance by comparing the keywords provided by authors in the CoRL’22 and report characteristics of the generated keywords. Our study shows the F1 score of our proposed method was ten times better than that of previous studies, and 42.7% of the generated keywords are relevant to author-defined keywords.
CSCW

Exploring the Community of Model Publishers on TensorFlow Hub

Taewon Yoo, Minki Chun, Yunjung Bae, Soohyun Kwon, and Hyunggu Jung

In Companion Publication of the 2022 Conference on Computer Supported Cooperative Work and Social Computing 2022

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We explore the community of AI model publishers on TensorFlow Hub (TF Hub). While researchers identified the challenges AI model publishers and AI model users faced, little is known about how they interact with each other in an online community. The analysis of the metadata recorded on TF Hub revealed the models that the AI model publishers uploaded. Also, we found out how the models published by the AI model publishers were shared with other people on TF Hub. To our knowledge, this is the first attempt to explore the online community of AI model publishers sharing their models with each other.
ECSCW

Toward an AI-assisted Assessment Tool to Support Online Art Therapy Practices: A Pilot Study

Woosuk Seo, Joonyoung Jun, Minki Chun, Hyeonhak Jeong, Sungmin Na, Woohyun Cho, Saeri Kim, and Hyunggu Jung

In Proceedings of 20th European Conference on Computer-Supported Cooperative Work 2022

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Artificial intelligence (AI) has been widely used to assist art therapists with artwork assessments by providing objective information. While prior studies showed that AI-assisted tools are feasible to improve drawing analysis in in-person art therapy practices, the use of those tools in online art therapy is still under-examined. To fill the gap, we created a prototype of an AI-assisted tool for online therapy in a House-Tree-Person (HTP) test scenario and ran lab-based usability sessions with 10 art therapists in which they used our proposed prototype to complete predefined tasks. We then conducted semi-structured interviews with the participants to understand their acceptance and concerns about the prototype. The findings revealed the unique needs of art therapists and opportunities of using AI-assisted tools to improve online art therapy practices. Based on these findings, we suggest implications for creating AI-assisted tools that meet specific needs of art therapists in online therapy sessions, and further discuss future directions of research about AI-assisted tools for art therapists in online settings.
CSCW

Exploring the Experiences of Student Volunteer and Student Volunteer Chair Communities at Academic Conferences

Subin Park, Heejae Jung, Jae Won Choi, Jihyeon Park, Joonyoung Jun, Minki Chun, and Hyunggu Jung

Proceedings of the ACM on Human-Computer Interaction Oct 2021

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CSCW

Exploring the Experiences of Streamers with Visual Impairments

Joonyoung Jun, Woosuk Seo, Jihyeon Park, Subin Park, and Hyunggu Jung

Proceedings of the ACM on Human-Computer Interaction Oct 2021

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IUI

MonoPass: A Password Manager without Master Password Authentication

Hyeonhak Jeong, and Hyunggu Jung

In 26th International Conference on Intelligent User Interfaces-Companion Apr 2021

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IUI

LectYS: A System for Summarizing Lecture Videos on YouTube

Taewon Yoo, Hyewon Jeong, Donghwan Lee, and Hyunggu Jung

In 26th International Conference on Intelligent User Interfaces-Companion Apr 2021

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JMIR

A novel food record app for dietary assessments among older adults with type 2 diabetes: development and usability study

Hyunggu Jung , George Demiris, Peter Tarczy-Hornoch, Mark Zachry, and others

JMIR Formative Research Feb 2021

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ASSETS

Exploring the community of blind or visually impaired people on YouTube

Woosuk Seo, and Hyunggu Jung

In Proceedings of the 19th International ACM SIGACCESS Conference on Computers and Accessibility Oct 2017

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CSCW

Personas and scenarios to design technologies for North Korean defectors with depression

Hyunggu Jung , Woosuk Seo, and Michelle Cha

In Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing Feb 2017

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AMIA

Development of a novel markov chain model for the prediction of head and neck squamous cell carcinoma dissemination

Hyunggu Jung , Anthony Law, Eli Grunblatt, Lucy L Wang, Aaron Kusano, Jose LV Mejino Jr, and Mark E Whipple

In AMIA Annual Symposium Proceedings Nov 2016

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AMIA

Nurse informaticians report low satisfaction and multi-level concerns with electronic health records: results from an international survey

Maxim Topaz, Charlene Ronquillo, Laura-Maria Peltonen, Lisiane Pruinelli, Raymond Francis Sarmiento, Martha K Badger, Samira Ali, Adrienne Lewis, Mattias Georgsson, Eunjoo Jeon, and others

In AMIA Annual Symposium Proceedings Nov 2016

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CHI

’MASTerful’Matchmaking in Service Transactions: Inferred Abilities, Needs and Interests versus Activity Histories

Hyunggu Jung , Victoria Bellotti, Afsaneh Doryab, Dean Leitersdorf, Jiawei Chen, Benjamin V Hanrahan, Sooyeon Lee, Dan Turner, Anind K Dey, and John M Carroll

In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems May 2016

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AMIA

Biological Model Development as an Opportunity to Provide Content Auditing for the Foundational Model of Anatomy Ontology

Lucy L Wang, Eli Grunblatt, Hyunggu Jung , Ira J Kalet, and Mark E Whipple

In AMIA Annual Symposium Proceedings Nov 2015

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SIGDOC

Designing tools to support advanced users in new forms of social media interaction

Hyunggu Jung , Sungsoo Hong, Perry Meas, and Mark Zachry

In Proceedings of the 33rd Annual International Conference on the Design of Communication Jul 2015

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