arXiv subjects

Kayur Patel

Publications and source records attributed to Kayur Patel.

4 recordsLinked to original sources

arXiv2026

Negotiating Ontological Boundaries in User-Authored Personal Sensing Systems

Designed artifacts are ontological, shaping, and at times limiting, what becomes possible or imaginable. One path toward mitigating such foreclosures is giving people power over how systems are designed and built. Despite decades of scholarship around systems that enable such authorship, these systems are often evaluated on whether or not they are usable, useful, or technically feasible, leaving questions of ontological boundary negotiation, unexamined. We design two open-ended probes that utilize a Wizard of Oz technique to enable the experience of training a personalized machine learning system on phenomena people define themselves. In a week-long exploratory study, participants use one of two probes in the course of their everyday lives. We identify four sites where ontological boundaries were negotiated; the boundaries of a phenomena, the subject as part of relations, what is signal and what is noise, and the objectivity of data. We offer starting points for supporting boundary negotiation through design and discuss open-ended probes as a method for ontological design.

cs.HC↗

arXiv2022

Jury Learning: Integrating Dissenting Voices into Machine Learning Models

Whose labels should a machine learning (ML) algorithm learn to emulate? For ML tasks ranging from online comment toxicity to misinformation detection to medical diagnosis, different groups in society may have irreconcilable disagreements about ground truth labels. Supervised ML today resolves these label disagreements implicitly using majority vote, which overrides minority groups' labels. We introduce jury learning, a supervised ML approach that resolves these disagreements explicitly through the metaphor of a jury: defining which people or groups, in what proportion, determine the classifier's prediction. For example, a jury learning model for online toxicity might centrally feature women and Black jurors, who are commonly targets of online harassment. To enable jury learning, we contribute a deep learning architecture that models every annotator in a dataset, samples from annotators' models to populate the jury, then runs inference to classify. Our architecture enables juries that dynamically adapt their composition, explore counterfactuals, and visualize dissent.

cs.HC↗

arXiv2021

Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels

The confusion matrix, a ubiquitous visualization for helping people evaluate machine learning models, is a tabular layout that compares predicted class labels against actual class labels over all data instances. We conduct formative research with machine learning practitioners at Apple and find that conventional confusion matrices do not support more complex data-structures found in modern-day applications, such as hierarchical and multi-output labels. To express such variations of confusion matrices, we design an algebra that models confusion matrices as probability distributions. Based on this algebra, we develop Neo, a visual analytics system that enables practitioners to flexibly author and interact with hierarchical and multi-output confusion matrices, visualize derived metrics, renormalize confusions, and share matrix specifications. Finally, we demonstrate Neo's utility with three model evaluation scenarios that help people better understand model performance and reveal hidden confusions.

cs.HC↗

arXiv2020

mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks

We aim to increase the flexibility at which a data worker can choose the right tool for the job, regardless of whether the tool is a code library or an interactive graphical user interface (GUI). To achieve this flexibility, we extend computational notebooks with a new API mage, which supports tools that can represent themselves as both code and GUI as needed. We discuss the design of mage as well as design opportunities in the space of flexible code/GUI tools for data work. To understand tooling needs, we conduct a study with nine professional practitioners and elicit their feedback on mage and potential areas for flexible code/GUI tooling. We then implement six client tools for mage that illustrate the main themes of our study findings. Finally, we discuss open challenges in providing flexible code/GUI interactions for data workers.

cs.HC↗