About

Alex Kale is an Assistant Professor of Computer Science and Data Science at the University of Chicago. His research focuses on advancing the field of computer science through contributions to data science and related areas. As part of the faculty, he is involved in exploring interdisciplinary applications within computer science, contributing to the department's mission of defining and building the future of the field from theory to applications and from science to society.

Research topics

• Computer Science
• Software engineering
• Programming language
• Data science
• Mathematics
• Epistemology
• Psychology
• Econometrics
• Social psychology
• Statistics

Selected publications

• Designing for Disclosure in Data Visualizations

  ArXiv.org · 2025-08-11

  preprintOpen accessSenior author

  Visualizing data often entails data transformations that can reveal and hide information, operations we dub disclosure tactics. Whether designers hide information intentionally or as an implicit consequence of other design choices, tools and frameworks for visualization offer little explicit guidance on disclosure. To systematically characterize how visualizations can limit access to an underlying dataset, we contribute a content analysis of 425 examples of visualization techniques sampled from academic papers in the visualization literature, resulting in a taxonomy of disclosure tactics. Our taxonomy organizes disclosure tactics based on how they change the data representation underlying a chart, providing a systematic way to reason about design trade-offs in terms of what information is revealed, distorted, or hidden. We demonstrate the benefits of using our taxonomy by showing how it can guide reasoning in design scenarios where disclosure is a first-order consideration. Adopting disclosure as a framework for visualization research offers new perspective on authoring tools, literacy, uncertainty communication, personalization, and ethical design.

  PublisherOA PDFDOI

• Underreporting of AI Use: The Role of Social Desirability Bias

  SSRN Electronic Journal · 2025-01-01 · 3 citations

  preprintOpen access
  PublisherDOI

• Underspecified Human Decision Experiments Considered Harmful

  2025-04-24 · 2 citations

  articleOpen access
  PublisherDOI

• Designing for Disclosure in Data Visualizations

  IEEE Transactions on Visualization and Computer Graphics · 2025-11-25

  articleSenior author

  Visualizing data often entails data transformations that can reveal and hide information, operations we dub disclosure tactics. Whether designers hide information intentionally or as an implicit consequence of other design choices, tools and frameworks for visualization offer little explicit guidance on disclosure. To systematically characterize how visualizations can limit access to an underlying dataset, we contribute a content analysis of 425 examples of visualization techniques sampled from academic papers in the visualization literature, resulting in a taxonomy of disclosure tactics. Our taxonomy organizes disclosure tactics based on how they change the data representation underlying a chart, providing a systematic way to reason about design trade-offs in terms of what information is revealed, distorted, or hidden. We demonstrate the benefits of using our taxonomy by showing how it can guide reasoning in design scenarios where disclosure is a first-order consideration. Adopting disclosure as a framework for visualization research offers new perspective on authoring tools, literacy, uncertainty communication, personalization, and ethical design.

  PublisherDOI

• Toward a Logic of Generalization about Visualization as a Decision Aid

  ArXiv.org · 2025-08-08

  preprintOpen access1st authorCorresponding

  Visualization as a discipline often grapples with generalization by reasoning about how study results on the efficacy of a tool in one context might apply to another context. This work offers an account of the logic of generalization in visualization research and argues that it struggles in particular with applications of visualization as a decision aid. We use decision theory to define the dimensions on which decision problems can vary, and we present an analysis of heterogeneity in scenarios where visualization supports decision-making. Our findings identify utility as a focal and under-examined concept in visualization research on decision-making, demonstrating how the visualization community's logic of generalization might benefit from using decision theory as a lens for understanding context variation.

  PublisherOA PDFDOI

• VMC: A Grammar for Visualizing Statistical Model Checks

  IEEE Transactions on Visualization and Computer Graphics · 2024-09-30 · 1 citations

  article

  Visualizations play a critical role in validating and improving statistical models. However, the design space of model check visualizations is not well understood, making it difficult for authors to explore and specify effective graphical model checks. VMC defines a model check visualization using four components: (1) samples of distributions of checkable quantities generated from the model, including predictive distributions for new data and distributions of model parameters; (2) transformations on observed data to facilitate comparison; (3) visual representations of distributions; and (4) layouts to facilitate comparing model samples and observed data. We contribute an implementation of VMC as an R package. We validate VMC by reproducing a set of canonical model check examples, and show how using VMC to generate model checks reduces the edit distance between visualizations relative to existing visualization toolkits. The findings of an interview study with three expert modelers who used VMC highlight challenges and opportunities for encouraging exploration of correct, effective model check visualizations.

  PublisherDOI

• What Can Interactive Visualization Do for Participatory Budgeting in Chicago?

  IEEE Transactions on Visualization and Computer Graphics · 2024-09-09 · 3 citations

  article1st authorCorresponding

  Participatory budgeting (PB) is a democratic approach to allocating municipal spending that has been adopted in many places in recent years, including in Chicago. Current PB voting resembles a ballot where residents are asked which municipal projects, such as school improvements and road repairs, to fund with a limited budget. In this work, we ask how interactive visualization can benefit PB by conducting a design probe-based interview study (N=13) with policy workers and academics with expertise in PB, urban planning, and civic HCI. Our probe explores how graphical elicitation of voter preferences and a dashboard of voting statistics can be incorporated into a realistic PB tool. Through qualitative analysis, we find that visualization creates opportunities for city government to set expectations about budget constraints while also granting their constituents greater freedom to articulate a wider range of preferences. However, using visualization to provide transparency about PB requires efforts to mitigate potential access barriers and mistrust. We call for more visualization professionals to help build civic capacity by working in and studying political systems.

  PublisherDOI

• What Can Interactive Visualization do for Participatory Budgeting in Chicago?

  arXiv (Cornell University) · 2024-07-29 · 1 citations

  preprintOpen access1st authorCorresponding

  Participatory budgeting (PB) is a democratic approach to allocating municipal spending that has been adopted in many places in recent years, including in Chicago. Current PB voting resembles a ballot where residents are asked which municipal projects, such as school improvements and road repairs, to fund with a limited budget. In this work, we ask how interactive visualization can benefit PB by conducting a design probe-based interview study (N=13) with policy workers and academics with expertise in PB, urban planning, and civic HCI. Our probe explores how graphical elicitation of voter preferences and a dashboard of voting statistics can be incorporated into a realistic PB tool. Through qualitative analysis, we find that visualization creates opportunities for city government to set expectations about budget constraints while also granting their constituents greater freedom to articulate a wider range of preferences. However, using visualization to provide transparency about PB requires efforts to mitigate potential access barriers and mistrust. We call for more visualization professionals to help build civic capacity by working in and studying political systems.

  PublisherOA PDFDOI

• VMC: A Grammar for Visualizing Statistical Model Checks

  arXiv (Cornell University) · 2024-08-29 · 1 citations

  preprintOpen access

  Visualizations play a critical role in validating and improving statistical models. However, the design space of model check visualizations is not well understood, making it difficult for authors to explore and specify effective graphical model checks. VMC defines a model check visualization using four components: (1) samples of distributions of checkable quantities generated from the model, including predictive distributions for new data and distributions of model parameters; (2) transformations on observed data to facilitate comparison; (3) visual representations of distributions; and (4) layouts to facilitate comparing model samples and observed data. We contribute an implementation of VMC as an R package. We validate VMC by reproducing a set of canonical model check examples, and show how using VMC to generate model checks reduces the edit distance between visualizations relative to existing visualization toolkits. The findings of an interview study with three expert modelers who used VMC highlight challenges and opportunities for encouraging exploration of correct, effective model check visualizations.

  PublisherOA PDFDOI

• Underspecified Human Decision Experiments Considered Harmful

  arXiv (Cornell University) · 2024-01-25

  preprintOpen access

  Decision-making with information displays is a key focus of research in areas like human-AI collaboration and data visualization. However, what constitutes a decision problem, and what is required for an experiment to conclude that decisions are flawed, remain imprecise. We present a widely applicable definition of a decision problem synthesized from statistical decision theory and information economics. We claim that to attribute loss in human performance to bias, an experiment must provide the information that a rational agent would need to identify the normative decision. We evaluate whether recent empirical research on AI-assisted decisions achieves this standard. We find that only 10 (26%) of 39 studies that claim to identify biased behavior presented participants with sufficient information to make this claim in at least one treatment condition. We motivate the value of studying well-defined decision problems by describing a characterization of performance losses they allow to be conceived.

  PublisherOA PDFDOI

Frequent coauthors

• Jessica Hullman

  Northwestern University

  20 shared

• Michael‐Paul Schallmo

  University of Minnesota

  20 shared

• Scott O. Murray

  University of Washington

  19 shared

• Raphael Bernier

  University of Washington

  19 shared

• Rachel Millin

  Bellevue Hospital Center

  18 shared

• Anastasia V. Flevaris

  University of Washington

  13 shared

• Matthew Kay

  Northwestern University

  13 shared

• Tamar Kolodny

  12 shared

Education

• Ph.D., Information Science

  University of Washington

  2022

• M.S., Information Science

  University of Washington

  2020

• B.S., Psychology, with minors in Music and Philosophy

  University of Washington

  2015

Awards & honors

• 2026 NSF Early CAREER Award
• 2023 Best paper honorable mention, CHI
• 2021 Best paper honorable mention, VIS
• 2020 Best paper, VIS