Research
My recent interests include designing Human-centered Machine Learning systems, focusing on the non-model aspects (easily ignored before) like data, domain experts, and human-in-the-loop. Specifically, I focused on structuring human influences in ML processes to enhance efficiency and maximize their intuition for reliable and fair AI.
2023
The Generative AI Paradox on LLM Evaluation: "What It Can Solve, It May Not Evaluate"
Juhyun Oh*, Eunsu Kim*, Inha Cha*, and Alice Oh.
EACL Student Workshop 2024
(* Three authors equally contributed.)
Unlocking the Tacit Knowledge of Data Work in Machine Learning
Inha Cha*, Juhyun Oh*, Cheul Young Park* , Jiyoon Han, and Hwalsuk Lee
CHI 2023 LBW (Late-Breaking Work).
(* Three authors equally contributed.)
2022
Human-centered Proposition for Structuring Data Construction
Inha Cha*, Juhyun Oh*, and Cheul Young Park*
HCAI Workshop @NeurIPS 2022, Accepted.
(* Three authors equally contributed.)
Unveiling human influences in dataset construction for Document Text OCR Systems
Juhyun Oh, Inha Cha, Jiyoon Han, Sungrae Park, and Hwalsuk Lee
CHI 2022 Workshop (Investigating Data Work Across Domains: New Perspectives on the Work of Creating Data).
2021
Inha Cha, Sung-In Kim, Hwajung Hong, Heejeong Yoo, Youn-kyung Lim
CHI 2021.
Inha Cha and Youn-kyung Lim
CHI 2021 Workshop (Co-design Resources for Ethics Education in HCI).
2020
Inha Cha, Sung-In Kim, Hwajung Hong, Heejeong Yoo, Youn-kyung Lim
INSAR (International Meeting for Autism Research) 2020.
2019
Sung-In Kim, Eunkyung Jo, Myeonghan Ryu, Inha Cha, Young-Ho Kim, Heejeong Yoo, and Hwajung Hong.
Pervasive Health 2019.
Sung-In Kim, Myeonghan Ryu, Eunkyung Jo, Inha Cha, Young-Ho Kim, Heejeong Yoo, and Hwajung Hong.
HCI Korea 2019.