arXiv subjects

Eun Jeong Kang

Publications and source records attributed to Eun Jeong Kang.

3 recordsLinked to original sources

arXiv2026

Novice Reliance Calibration in AI-Assisted Decision Making: The Role of Explanations and Self-Assessment

Artificial Intelligence (AI) tools are widely used to support decision making in tasks and domains where no immediate performance feedback is available. In these settings, users cannot learn to adjust their reliance behavior over time through trial and error. However, little is known about how novice users calibrate reliance on AI when external feedback is unavailable, or whether AI explanations can support calibration in its absence. We introduce reliance calibration as an organizing construct for studying how novice users dynamically adjust reliance behavior, and examine how AI explanations and meta-cognitive self-assessment shape it. Through a between-subjects study with 110 participants completing a clinical entity extraction task with AI assistance and limited performance feedback, we observe that novice users exhibit systematic drift toward over-reliance in the presence of explanations, while higher self-reported task understanding is associated with more selective reliance behavior. These results extend reliance calibration research into human-AI collaboration contexts without real-time performance signals and present actionable guidelines on designing AI tools that must support appropriate reliance in these settings.

cs.HC↗

arXiv2026

Policy or Community?: Supporting Individual Model Creators' Open Model Development in Model Marketplaces

Lightweight fine-tuning techniques and the rise of 'open' AI model marketplaces have enabled individuals to easily build and release generative models. Yet, this accessibility also raises risks, including the production of harmful and infringing content. While platforms offer policies and responsible AI tools, their effectiveness may be limited, as creators engage with partially open models that vary widely in openness and transparency. To understand how platform governance can better support responsible practices, we conducted semi-structured interviews with 19 individual model creators. We identified three regulatory needs shaped by creators' workflows: reducing downstream harms, recognizing creators' contributions and originality, and securing model ownership. Creators also repurpose RAI tools primarily for self-protection and visibility, and their sense of responsibility is deeply shaped by community norms rather than formal policies. We argue that platforms' governance decisions must consider how policy interventions shape the practices and motivations of individual creators.

cs.HC↗

arXiv2025

Proxona: Supporting Creators' Sensemaking and Ideation with LLM-Powered Audience Personas

A content creator's success depends on understanding their audience, but existing tools fail to provide in-depth insights and actionable feedback necessary for effectively targeting their audience. We present Proxona, an LLM-powered system that transforms static audience comments into interactive, multi-dimensional personas, allowing creators to engage with them to gain insights, gather simulated feedback, and refine content. Proxona distills audience traits from comments, into dimensions (categories) and values (attributes), then clusters them into interactive personas representing audience segments. Technical evaluations show that Proxona generates diverse dimensions and values, enabling the creation of personas that sufficiently reflect the audience and support data grounded conversation. User evaluation with 11 creators confirmed that Proxona helped creators discover hidden audiences, gain persona-informed insights on early-stage content, and allowed them to confidently employ strategies when iteratively creating storylines. Proxona introduces a novel creator-audience interaction framework and fosters a persona-driven, co-creative process.

cs.HC↗