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Performance analysis is critical in applied machine learning because it influences the models practitioners produce. Current performance analysis tools suffer from issues including obscuring important characteristics of model behavior and dissociating performance from data. In this work, we present Squares, a performance visualization for multiclass classification problems. Squares supports estimating common performance metrics while displaying instance-level distribution information necessary for helping practitioners prioritize efforts and access data. Our controlled study shows that practitioners can assess performance significantly faster and more accurately with Squares than a confusion matrix, a common performance analysis tool in machine learning.

We demonstrate the value of collecting semantic parse labels for knowledge base question answering. In particular, (1) unlike previous studies on small-scale datasets, we show that learning from labeled semantic parses significantly improves overall performance, resulting in absolute 5 point gain compared to learning from answers, (2) we show that with an appropriate user interface, one can obtain semantic parses with high accuracy and at a cost comparable or lower than obtaining just answers, and (3) we have created and shared the largest semantic-parse labeled dataset to date in order to advance research in question answering.

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Machine learning (ML) has become increasingly influential to human society, yet the primary advancements and applications of ML are driven by research in only a few computational disciplines. Even applications that affect or analyze human behaviors and social structures are often developed with limited input from experts outside of computational fields. Social scientists—experts trained to examine and explain the complexity of human behavior and interactions in the world—have considerable expertise to contribute to the development of ML applications for human-generated data, and their analytic practices could benefit from more human-centered ML methods. Although a few researchers have highlighted some gaps between ML and social sciences [51, 57, 70], most discussions only focus on quantitative methods. Yet many social science disciplines rely heavily on qualitative methods to distill patterns that are challenging to discover through quantitative data. One common analysis method for qualitative data is qualitative coding . In this article, we highlight three challenges of applying ML to qualitative coding. Additionally, we utilize our experience of designing a visual analytics tool for collaborative qualitative coding to demonstrate the potential in using ML to support qualitative coding by shifting the focus to identifying ambiguity. We illustrate dimensions of ambiguity and discuss the relationship between disagreement and ambiguity. Finally, we propose three research directions to ground ML applications for social science as part of the progression toward human-centered machine learning.