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Researcher profile

Changkun Ou

Changkun Ou contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Human-Computer InteractionArtificial IntelligenceGraphics

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Published work

4 published item(s)

preprint2026arXiv

Progressive Autonomy as Preference Learning: A Formalization of Trust Calibration for Agentic Tool Use

We formalize trust calibration for agentic tool use (deciding when an automated agent's proposed action may execute autonomously versus require human approval) as a preference-learning problem. A policy gateway maintains a Gaussian-process posterior over a latent human risk-tolerance function, observed through a probit likelihood on binary approve/deny feedback, and escalates to the human exactly where the approval outcome is most uncertain. We show this is structurally an instance of Preferential Bayesian Optimization, inheriting its inference machinery (approximate Gaussian-process classification) and its sample-efficiency argument (uncertainty-targeted querying), while differing in objective: classifying an action space into allow/block/ask regions rather than optimizing a design.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Generative 3D Animation Pipelines: Automating Facial Retargeting Workflows

Design tools in the 3D industry, while powerful, are still tedious and labor-intensive when it comes to bringing a creative idea for a visual effect to life. In this position paper, we discussed how an infamous generative synthetic media, deepfakes, could be of use and embedded into common sophisticated 3D workflows to reduce user workloads in areas such as 3D model editing, material design, and character animation. As a case discussion, we also prototyped a tool to address the retargeting problem in character animation. Although deepfakes themselves have received a negative public image, the results of our interviews with field experts are unexpectedly positive in regard to our tool that utilizes deepfake algorithms. Lastly, we also discussed our experience and observed design practices to put deepfakes to good use, including how we could avoid potential misuses directly by design, how this design changes user interactions, and subsequent open issues.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The Human in the Infinite Loop: A Case Study on Revealing and Explaining Human-AI Interaction Loop Failures

Interactive AI systems increasingly employ a human-in-the-loop strategy. This creates new challenges for the HCI community when designing such systems. We reveal and investigate some of these challenges in a case study with an industry partner, and developed a prototype human-in-the-loop system for preference-guided 3D model processing. Two 3D artists used it in their daily work for 3 months. We found that the human-AI loop often did not converge towards a satisfactory result and designed a lab study (N=20) to investigate this further. We analyze interaction data and user feedback through the lens of theories of human judgment to explain the observed human-in-the-loop failures with two key insights: 1) optimization using preferential choices lacks mechanisms to deal with inconsistent and contradictory human judgments; 2) machine outcomes, in turn, influence future user inputs via heuristic biases and loss aversion. To mitigate these problems, we propose descriptive UI design guidelines. Our case study draws attention to challenging and practically relevant imperfections in human-AI loops that need to be considered when designing human-in-the-loop systems.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Modeling Web Browsing Behavior across Tabs and Websites with Tracking and Prediction on the Client Side

Clickstreams on individual websites have been studied for decades to gain insights into user interests and to improve website experiences. This paper proposes and examines a novel sequence modeling approach for web clickstreams, that also considers multi-tab branching and backtracking actions across websites to capture the full action sequence of a user while browsing. All of this is done using machine learning on the client side to obtain a more comprehensive view and at the same time preserve privacy. We evaluate our formalism with a model trained on data collected in a user study with three different browsing tasks based on different human information seeking strategies from psychological literature. Our results show that the model can successfully distinguish between browsing behaviors and correctly predict future actions. A subsequent qualitative analysis identified five common web browsing patterns from our collected behavior data, which help to interpret the model. More generally, this illustrates the power of overparameterization in ML and offers a new way of modeling, reasoning with, and prediction of observable sequential human interaction behaviors.

0 citations0 reviewsNo code linkedOpen access