We demonstrate an AI assisted data labeling system which applies unsupervised and semi-supervised machine learning to facilitate accurate and efficient labeling of large data sets. Our system (1) applies representative data sampling and active learning in order to seed and maintain a semi-supervised learner that assists the human labeler (2) provides visual labeling assistance and optimizes labeling mechanics using predicted labels (3) seamlessly updates and learns from ongoing human labeling activity (4) captures and presents metrics that indicate the quality of labeling assistance, and (5) provides an interactive auto labeling interface to group, review and apply predicted labels in a scalable manner.

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 automate groups of conflict resolution tasks. However, little is known about whether and how people will rely on automation during conflict resolution. Currently, automation commonly uses labelers' majority vote labels for conflict resolution, as the top chosen label by most labelers is often correct. We envisioned a system where an AI would assist in finding cases where the labeler majority vote was wrong and where automation is supported for batches or groups of conflicts. In order to understand whether humans could use labeler and AI information effectively, we investigated how and when users rely on labeler and AI information and on automated group conflict resolution. We ran a study with 144 Mechanical Turk workers. We found that automation increased users' accuracy/time, use of automated conflict resolution was relatively similar regardless of whether the automation was based on labeler or AI selected labels, and providing labeler and AI selected labels may reduce inappropriate reliance on automation.

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.

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 binary decision tasks. However, the role of AI assistance in more complex data-labeling scenarios with a larger set of labels has not yet been explored. We designed an AI labeling assistant that uses a semi-supervised learning algorithm to predict the most probable labels for each example. We leverage these predictions to provide assistance in two ways: (i) providing a label recommendation and (ii) reducing the labeler’s decision space by focusing their attention on only the most probable labels. We conducted a user study (n=54) to evaluate an AI-assisted interface for data labeling in this context. Our results highlight that the AI assistance improves both labeler accuracy and speed, especially when the labeler finds the correct label in the reduced label space. We discuss findings related to the presentation of AI assistance and design implications for intelligent labeling interfaces.

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.

Chatbots are becoming quite popular, with many brands developing conversational experiences using platforms such as IBM's Watson Assistant and Facebook Messenger. However, previous research reveals that users' expectations of what conversational agents can understand and do far outpace their actual technical capabilities. Our work seeks to bridge the gap between these expectations and reality by designing a fun learning experience with several goals: explaining how chatbots work by mapping utterances to a set of intents, teaching strategies for avoiding conversational breakdowns, and increasing desire to use chatbots by creating feelings of empathy toward them. Our experience, called BigBlueBot, consists of interactions with two chatbots in which breakdowns occur and the user (or chatbot) must recover using one or more repair strategies. In a Mechanical Turk evaluation (N=88), participants learned strategies for having successful human-agent interactions, reported feelings of empathy toward the chatbots, and expressed a desire to interact with chatbots in the future.

This paper studies the consistency of the kernel-based neural ranking model K-NRM, a recent state-of-the-art neural IR model, which is important for reproducible research and deployment in the industry. We find that K-NRM has low variance on relevance-based metrics across experimental trials. In spite of this low variance in overall performance, different trials produce different document rankings for individual queries. The main source of variance in our experiments was found to be different latent matching patterns captured by K-NRM. In the IR-customized word embeddings learned by K-NRM, the query-document word pairs follow two different matching patterns that are equally effective, but align word pairs differently in the embedding space. The different latent matching patterns enable a simple yet effective approach to construct ensemble rankers, which improve K-NRM's effectiveness and generalization abilities.