12 citations · 16 across the 7 of their papers we have counts for
7 papers
The Values of Value in AI Adoption: Rethinking Efficiency in UX Designers' Workplaces
Inha Cha, Catherine Wieczorek, Richmond Y. Wong
Although organizations increasingly position AI adoption as a pathway to competitiveness and innovation, organizations' perspectives on productivity and efficiency often clash with…
"My body is not your Porn": Identifying Trends of Harm and Oppression through a Sociotechnical Genealogy of Digital Sexual Violence in South Korea
Inha Cha, Yeonju Jang, Haesoo Kim +3
Ever since the introduction of internet technologies in South Korea, digital sexual violence (DSV) has been a persistent and pervasive problem. Evolving alongside digital technolog…
Culture is Everywhere: A Call for Intentionally Cultural Evaluation
Juhyun Oh, Inha Cha, Michael Saxon +3
The prevailing ``trivia-centered paradigm'' for evaluating the cultural alignment of large language models (LLMs) is increasingly inadequate as these models become more advanced an…
Uncovering Factor Level Preferences to Improve Human-Model Alignment
Juhyun Oh, Eunsu Kim, Jiseon Kim +4
Large language models (LLMs) often exhibit tendencies that diverge from human preferences, such as favoring certain writing styles or producing overly verbose outputs. While crucia…
Ethics Pathways: A Design Activity for Reflecting on Ethics Engagement in HCI Research
Inha Cha, Ajit G. Pillai, Richmond Y. Wong
This paper introduces Ethics Pathways, a design activity aimed at understanding HCI and design researchers' ethics engagements and flows during their research process. Despite a st…
The Generative AI Paradox on Evaluation: What It Can Solve, It May Not Evaluate
Juhyun Oh, Eunsu Kim, Inha Cha +1
This paper explores the assumption that Large Language Models (LLMs) skilled in generation tasks are equally adept as evaluators. We assess the performance of three LLMs and one op…
The Grind for Good Data: Understanding ML Practitioners' Struggles and Aspirations in Making Good Data
Inha Cha, Juhyun Oh, Cheul Young Park +2
We thought data to be simply given, but reality tells otherwise; it is costly, situation-dependent, and muddled with dilemmas, constantly requiring human intervention. The ML commu…