arXiv subjects

Inha Cha

Publications and source records attributed to Inha Cha.

7 recordsLinked to original sources

arXiv2026

The Values of Value in AI Adoption: Rethinking Efficiency in UX Designers' Workplaces

Although organizations increasingly position AI adoption as a pathway to competitiveness and innovation, organizations' perspectives on productivity and efficiency often clash with workers' perspectives on AI's economic and social value. Through design workshops with 15 UX designers, we examine how AI adoption unfolds across individual, team, and organizational scales. At the individual level, designers weighed efficiency, skill development, and professional worth. At the team level, they negotiated collaboration, responsibility, and rigor. At the organizational level, adoption was shaped by compliance requirements and organizational norms. Across these scales, discourses of efficiency carried social and ethical dimensions of responsibility, trust, and autonomy. We view adoption as a site where roles, relationships, and power are reconfigured. We argue that AI adoption should be understood as a process of negotiating values, and call for future work examining how AI systems redistribute responsibility among team members, while understanding how such shifts could strengthen worker agency.

cs.CY↗

arXiv2026

"My body is not your Porn": Identifying Trends of Harm and Oppression through a Sociotechnical Genealogy of Digital Sexual Violence in South Korea

Ever since the introduction of internet technologies in South Korea, digital sexual violence (DSV) has been a persistent and pervasive problem. Evolving alongside digital technologies, the severity and scale of violence have grown consistently, leading to widespread public concern. In this paper, we present four eras of image-based DSV in South Korea, spanning from the early internet era of the 1990s to the deepfake scandals in the mid-2020s. Drawing from media coverage, legal documents, and academic literature, we elucidate forms and characteristics of DSV cases in each era, tracing how entrenched misogyny is reconfigured and amplified through evolving technologies, alongside shifting legislative measures. Taking a genealogical approach to read prominent cases of different eras, our analysis identifies three constitutive and interconnected dimensions of DSV: (1) the homo-social fabrication of "obscenity", wherein victims' imagery becomes collectively framed as obscene through participatory practices in male-dominant networks; (2) the increasing imperceptibility of violence, as technologies foreclose victims' ability to perceive harm; and (3) the commercialization of abuse through decentralized economic infrastructures. We suggest future directions for CSCW research, and further reflect on the value of the genealogical method in enabling non-linear understanding of DSV as dynamically evolving sociotechnical configurations of harm.

cs.HC↗

arXiv2025

Culture is Everywhere: A Call for Intentionally Cultural Evaluation

The prevailing ``trivia-centered paradigm'' for evaluating the cultural alignment of large language models (LLMs) is increasingly inadequate as these models become more advanced and widely deployed. Existing approaches typically reduce culture to static facts or values, testing models via multiple-choice or short-answer questions that treat culture as isolated trivia. Such methods neglect the pluralistic and interactive realities of culture, and overlook how cultural assumptions permeate even ostensibly ``neutral'' evaluation settings. In this position paper, we argue for \textbf{intentionally cultural evaluation}: an approach that systematically examines the cultural assumptions embedded in all aspects of evaluation, not just in explicitly cultural tasks. We systematically characterize the what, how, and circumstances by which culturally contingent considerations arise in evaluation, and emphasize the importance of researcher positionality for fostering inclusive, culturally aligned NLP research. Finally, we discuss implications and future directions for moving beyond current benchmarking practices, discovering important applications that we don't know exist, and involving communities in evaluation design through HCI-inspired participatory methodologies.

cs.CL↗

arXiv2024

Uncovering Factor Level Preferences to Improve Human-Model Alignment

Large language models (LLMs) often exhibit tendencies that diverge from human preferences, such as favoring certain writing styles or producing overly verbose outputs. While crucial for improvement, identifying the factors driving these misalignments remains challenging due to existing evaluation methods' reliance on coarse-grained comparisons and lack of explainability. To address this, we introduce PROFILE, an automated framework to uncover and measure factor-level preference alignment of humans and LLMs. Using PROFILE, we analyze preference alignment across three key tasks: summarization, instruction-following, and document-based QA. We find a significant discrepancy: while LLMs show poor factor-level alignment with human preferences when generating texts, they demonstrate strong alignment in discrimination tasks. We demonstrate how leveraging the identified generation-discrimination gap can be used to improve LLM alignment through multiple approaches, including fine-tuning with self-guidance. Our work highlights the value of factor-level analysis for identifying hidden misalignments and provides a practical framework for improving LLM-human preference alignment.

cs.CL↗

arXiv2024

Ethics Pathways: A Design Activity for Reflecting on Ethics Engagement in HCI Research

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 strong ethical commitment in these fields, challenges persist in grasping the complexity of researchers' engagement with ethics -- practices conducted to operationalize ethics -- in situated institutional contexts. Ethics Pathways, developed through six playtesting sessions, offers a design approach to understanding the complexities of researchers' past ethics engagements in their work. This activity involves four main tasks: recalling ethical incidents; describing stakeholders involved in the situation; recounting their actions or speculative alternatives; and reflection and emotion walk-through. The paper reflects on the role of design decisions and facilitation strategies in achieving these goals. The design activity contributes to the discourse on ethical HCI research by conceptualizing ethics engagement as a part of ongoing research processing, highlighting connections between individual affective experiences, social interactions across power differences, and institutional goals.

cs.CY↗

arXiv2024

The Generative AI Paradox on Evaluation: What It Can Solve, It May Not Evaluate

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 open-source LM in Question-Answering (QA) and evaluation tasks using the TriviaQA (Joshi et al., 2017) dataset. Results indicate a significant disparity, with LLMs exhibiting lower performance in evaluation tasks compared to generation tasks. Intriguingly, we discover instances of unfaithful evaluation where models accurately evaluate answers in areas where they lack competence, underscoring the need to examine the faithfulness and trustworthiness of LLMs as evaluators. This study contributes to the understanding of "the Generative AI Paradox" (West et al., 2023), highlighting a need to explore the correlation between generative excellence and evaluation proficiency, and the necessity to scrutinize the faithfulness aspect in model evaluations.

cs.CL↗

arXiv2022

The Grind for Good Data: Understanding ML Practitioners' Struggles and Aspirations in Making Good Data

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 community's focus on quality data is increasing in the same vein, as good data is vital for successful ML systems. Nonetheless, few works have investigated the dataset builders and the specifics of what they do and struggle to make good data. In this study, through semi-structured interviews with 19 ML experts, we present what humans actually do and consider in each step of the data construction pipeline. We further organize their struggles under three themes: 1) trade-offs from real-world constraints; 2) harmonizing assorted data workers for consistency; 3) the necessity of human intuition and tacit knowledge for processing data. Finally, we discuss why such struggles are inevitable for good data and what practitioners aspire, toward providing systematic support for data works.

cs.HC↗