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HUMAN-CENTERED ARTIFICIAL INTELLIGENCE LAB



HCAIL group photo at CSCW’23 in October 2023!

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.

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INTERESTED IN JOINING HCAIL AS A MS OR PHD STUDENT?

We have an opening position for a fully-funded MS and PhD student starting Fall
2024. 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 2024, we aim to admit up
to 2-3 new MS or PhD students.

HCAIL 연구실 자료 참고바랍니다: [슬라이드] [영상]

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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.

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NEWS

Feb 2024 Congratulations! The team HCAIL organized by our lab members including
Hyunmin Lee, SeungYoung Oh, Yunseo Moon, & Hyunggu Jung won the Excellent Prize
at the Barrier Free App Development Contest in Korea! [video] Feb 2024
Congratulations! Hyunmin, SeungYoung and Yunseo won Encouragement Award at
서울시립대학교 실전문제연구팀 연구성과 공유한마당! Jan 2024 Congratulations! Taewon and Hyunggu awarded
Best Paper at HCI Korea 2024! Jan 2024 Four papers were presented at HCI Korea
2024 in Hongcheon, Korea. Jan 2024 Welcome! Yubin Kim joined HCAIL as a
undergraduate research intern!


SELECTED PUBLICATIONS


2023

 1. CSCW
    Toward Value Scenario Generation Through Large Language Models
    Hyunggu Jung, Woosuk Seo, Seokwoo Song, and 1 more author
    In Companion Publication of the 2023 Conference on Computer Supported
    Cooperative Work and Social Computing, Oct 2023
    
    Abs HTML PDF
    1
    1 Total citation
    1 Recent citation
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    We propose a method of generating value scenarios for design research by
    leveraging ChatGPT, an AI-powered chatbot based on large language models.
    Identifying the needs of a vulnerable population, such as North Korean
    defectors, is challenging for researchers. To address this, we introduce
    ChatGPT-generated value scenarios, an extension of scenario-based design
    that supports critical, systemic, long-term thinking in current design
    practice, technology development, and deployment. Using our proposed method,
    we created a prompt to generate value scenarios on ChatGPT. Based on our
    analysis of the generated scenarios, we identified that ChatGPT could
    generate plausible information about Value Implications. However, it lacks
    details on Pervasiveness and Systemic Effects. After discussing the
    limitations and opportunities of ChatGPT in generating value scenarios, we
    conclude with suggestions for how ChatGPT might be better used to generate
    value scenarios.

 2. CSCW
    Visualizing the Carbon Intensity of Machine Learning Inference for Image
    Analysis on TensorFlow Hub
    Taewon Yoo, Hyunmin Lee, SeungYoung Oh, and 2 more authors
    In Companion Publication of the 2023 Conference on Computer Supported
    Cooperative Work and Social Computing, Oct 2023
    
    Abs HTML PDF
    
    
    The increasing performance of machine learning (ML) models necessitates
    greater computing resources, contributing to rising carbon intensity in ML
    computing and raising concerns about computational equity. Previous studies
    focused on developing tools that enable model developers to view the carbon
    intensity of the ML models in the training process. Still, little is known
    about how to support ML developers in online communities to explore the
    carbon intensity of ML models during inference. We developed MIEV, a model
    inference emission visualizer, that supports ML developers on TensorFlow Hub
    to explore the carbon intensity of image domain models during the model
    Inference phase. We also provide insights into designing technologies that
    promote collaborative work among ML developers to drive sustainable AI
    development processes. To the best of our knowledge, this is the first
    attempt to interactively visualize the carbon intensity of ML models in
    online communities during the Inference phase.

 3. IUI
    WATAA: Web Alternative Text Authoring Assistant for Improving Web Content
    Accessibility
    Hyeonhak Jeong, Minki Chun, Hyunmin Lee, and 2 more authors
    In Companion Proceedings of the 28th International Conference on Intelligent
    User Interfaces, Mar 2023
    
    Abs HTML PDF
    4
    4 Total citations
    4 Recent citations
    n/a Field Citation Ratio
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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.

 4. 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, Mar 2023
    
    Abs HTML PDF
    1
    1 Total citation
    1 Recent citation
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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.

 5. IUI
    Toward Keyword Generation Through Large Language Models
    Wanhae Lee, Minki Chun, Hyeonhak Jeong, and 1 more author
    In Companion Proceedings of the 28th International Conference on Intelligent
    User Interfaces, Mar 2023
    
    Abs HTML PDF
    6
    6 Total citations
    6 Recent citations
    n/a Field Citation Ratio
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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.


2022

 1. CSCW
    Exploring the Community of Model Publishers on TensorFlow Hub
    Taewon Yoo, Minki Chun, Yunjung Bae, and 2 more authors
    In Companion Publication of the 2022 Conference on Computer Supported
    Cooperative Work and Social Computing, Nov 2022
    
    Abs HTML PDF
    
    
    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.

 2. ECSCW
    Toward an AI-assisted Assessment Tool to Support Online Art Therapy
    Practices: A Pilot Study
    Woosuk Seo, Joonyoung Jun, Minki Chun, and 5 more authors
    In Proceedings of 20th European Conference on Computer-Supported Cooperative
    Work, Nov 2022
    
    Abs HTML PDF
    
    
    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.


2021

 1. CSCW
    Exploring the Experiences of Student Volunteer and Student Volunteer Chair
    Communities at Academic Conferences
    Subin Park, Heejae Jung, Jae Won Choi, and 4 more authors
    Proceedings of the ACM on Human-Computer Interaction, Oct 2021
    
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 2. CSCW
    Exploring the Experiences of Streamers with Visual Impairments
    Joonyoung Jun, Woosuk Seo, Jihyeon Park, and 2 more authors
    Proceedings of the ACM on Human-Computer Interaction, Oct 2021
    
    HTML
    
 3. 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
    
    HTML PDF
    
 4. IUI
    LectYS: A System for Summarizing Lecture Videos on YouTube
    Taewon Yoo, Hyewon Jeong, Donghwan Lee, and 1 more author
    In 26th International Conference on Intelligent User Interfaces-Companion,
    Apr 2021
    
    HTML PDF
    
 5. 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, and 1 more author
    JMIR Formative Research, Feb 2021
    
    HTML
    


2017

 1. 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
    
    HTML PDF
    
 2. 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
    
    HTML PDF
    


2016

 1. 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, and 4 more authors
    In AMIA Annual Symposium Proceedings, Nov 2016
    
    HTML PDF
    
 2. 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, and 8 more authors
    In AMIA Annual Symposium Proceedings, Nov 2016
    
    HTML PDF
    
 3. CHI
    ’MASTerful’Matchmaking in Service Transactions: Inferred Abilities, Needs
    and Interests versus Activity Histories
    Hyunggu Jung, Victoria Bellotti, Afsaneh Doryab, and 7 more authors
    In Proceedings of the 2016 CHI Conference on Human Factors in Computing
    Systems, May 2016
    
    HTML PDF
    


2015

 1. 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, and 2 more authors
    In AMIA Annual Symposium Proceedings, Nov 2015
    
    HTML PDF
    
 2. SIGDOC
    Designing tools to support advanced users in new forms of social media
    interaction
    Hyunggu Jung, Sungsoo Hong, Perry Meas, and 1 more author
    In Proceedings of the 33rd Annual International Conference on the Design of
    Communication, Jul 2015
    
    HTML PDF
    


© Copyright 2024 Human-Centered Artificial Intelligence Lab. Last updated: March
12, 2024.