AI-Assisted Human Labeling: Batching for Efficiency without Overreliance

Zahra Ashktorab, Michael Desmond, Josh Andres, Michael Muller, Narendra Nath Joshi, Michelle Brachman, Aabhas Sharma, Kristina Brimijoin, Qian Pan, Christine T. Wolf, Evelyn Duesterwald, Casey Dugan, Werner Geyer, Darrell Reimer

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

Abstract

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 participants to investigate labeler-AI-batching system interaction. We investigate the efficacy of the system when compared to a single-item labeling interface (i.e., labeling one record at-a-time), and evaluate the impact of batch labeling on accuracy and time. We further investigate the impact of AI algorithm quality and its effects on the labelers' overreliance, as well as potential mechanisms for mitigating it. Our work offers implications for the design of batch labeling systems and for work practices focusing on labeler-AI-batching system interaction.

Original languageEnglish
Article number89
JournalProceedings of the ACM on Human-Computer Interaction
Volume5
Issue numberCSCW1
DOIs
Publication statusPublished - 22 Apr 2021
Externally publishedYes

Fingerprint

Dive into the research topics of 'AI-Assisted Human Labeling: Batching for Efficiency without Overreliance'. Together they form a unique fingerprint.

Cite this