Michael Desmond

schoolIBMbar_chart#2030 Global Rank

Total Papers

HCI Rank

#2030

Research Areas

Years Active

Recent Publications

EvalAssist: Insights on Task-Specific Evaluations and AI-Assisted Judgment Strategy Preferences

With the broad availability of large language models and their ability to generate vast outputs using varied prompts and configurations, determining the best output for a given task requires an intensive evaluation process, one where machine learning practitioners must decide how to assess the outputs and then careful…

DIS '232nd AuthorHuman-LLM Collaboration +2

SME-in-the-loop: Interaction Preferences when Supervising Bots in Human-AI Communities

Subject matter experts play an important role in customer support communities by responding to user queries. Some communities have adopted chatbots in addition to SMEs to address commonly asked questions. Yet, SME-bot interactions, particularly teaching paradigms between SMEs and bots remain understudied. We investiga…

CSCW '223rd AuthorHuman-AI collaboration; Human-AI collaboration

Reliance and Automation for Human-AI Collaborative Data Labeling Conflict Resolution

Human data labeling with multiple labelers and the resulting conflict resolution remains the norm for many enterprise machine learning pipelines. Conflict resolution can be a time-intensive and costly process. Our goal was to study how human-AI collaboration can improve conflict resolution, by enabling users to automa…

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 '211st 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 '214th 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…

Quick Actions

Share this page

ios_share

X in mail

https://hci.top/en/authors/7673?source=all