Kristina Brimijoin

schoolIBMbar_chart#2031 Global Rank

Total Papers

HCI Rank

#2031

Research Areas

Years Active

Recent Publications

Controlling AI Agent Participation in Group Conversations: A Human-Centered Approach

Conversational AI agents are commonly applied within single-user, turn-taking scenarios. The interaction mechanics of these scenarios are trivial: when the user enters a message, the AI agent produces a response. However, the interaction dynamics are more complex within group settings. How should an agent behave in th…

CUI '242nd AuthorConversational Chatbots +1

Say What? Real-time Linguistic Guidance Supports Novices in Writing Utterances for Conversational Agent Training

Writing utterances to train conversational agents can be a challenging and time-consuming task, and usually requires substantial expertise, meaning that novices face a steep learning curve. We investigated whether novices could be guided to produce utterances that adhere to best practices via an intervention of real-t…

CUI '243rd AuthorConversational Chatbots +2

Towards Interactive Guidance for Writing Training Utterances for Conversational Agents

Improving conversational agents that are trained with supervised learning requires iteratively refining example intent training utterances based on chat log data. The difficulty of this process hinges on the quality of the initial example utterances used to train the intent before it was first deployed. Creating new i…

CSCW '215th+ AuthorData Work and AI

AI-Assisted Human Labeling: Batching for Efficiency without Overreliance

Human labeling of training data is often a time-consuming, expensive part of machine learning. In this paper, we study "batch labeling", an AI-assisted UX paradigm, that aids data labelers by allowing a single labeling action to apply to multiple records. We ran a large scale study on Mechanical Turk with 156 particip…

IUI '215th+ AuthorExplainable AI (XAI) +2

Increasing the Speed and Accuracy of Data Labeling Through an AI Assisted Interface

Labeling data is an important step in the supervised machine learning lifecycle. It is a laborious human activity comprised of repeated decision making: the human labeler decides which of several potential labels to apply to each example. Prior work has shown that providing AI assistance can improve the accuracy of bi…

CHI '215th+ AuthorComputational Methods in HCI +1

Designing Ground Truth and the Social Life of Labels

Ground-truth labeling is an important activity in machine learning. Many studies have examined how crowdworkers apply labels to records in machine learning datasets. However, there have been few studies that have examined the work of domain experts when their knowledge and expertise are needed to apply labels. We p…

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