About

Leilani Battle is an Associate Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington and co-director of the UW Interactive Data Lab. Her research interests focus on developing interactive data-intensive systems that can aid analysts in performing complex data exploration and analysis. Her current research is anchored in the field of databases, but utilizes research methodology and techniques from HCI and visualization to integrate data processing (databases) with interactive interfaces (HCI, visualization). Prof. Battle has received early career awards from both the database (2022 TCDE Rising Star Award) and visualization (2023 IEEE VGTC Significant New Researcher Award) research communities. She was named one of the 35 Innovators Under 35 by the MIT Technology Review in 2020 and was selected as a Sloan Fellow in 2023. Her research has been supported by several organizations, including Adobe, VMWare, Google, the ORAU, and the National Science Foundation (via the GRF, CISE CRII and CISE CAREER programs). In 2017, she completed a postdoc in the UW Interactive Data Lab. She holds an MS (2013) and PhD (2017) in Computer Science from MIT, where she was a member of the MIT Database Group, and a BS in Computer Engineering from UW (2011), where she was a member of the UW database group.

Research topics

• Data Mining
• Computer Science
• Database

Selected publications

• Evaluating Behavior Change Interventions for Responsible Data Science

  2026-04-13 · 1 citations

  articleOpen access

  The adoption of responsible data science (RDS) practices in AI development remains inadequate despite growing awareness of algorithmic harms. One measure of success is by observing practitioners’ behaviors – namely, their adoption of responsible sequences of behaviors in their model building practice. This paper evaluates two interventions for changing problematic behaviors: (i) a motivational priming intervention that introduces short, relevant stories, and (ii) a fairness toolkit (Aequitas)—to bridge the gap between ethical principles and practitioner behavior. Through a mixed-methods study with data scientists (N=12), we assess how these interventions influence fairness practices, model outcomes, and cognitive load across credit risk and income classification tasks. Results indicate that both interventions were efficient in promoting responsible data science behaviors and improving the delivered models’ fairness, while maintaining baseline accuracy. We argue that effective behavior change interventions must balance technical tooling with motivational scaffolding to provide actionable insights for fostering sustainable RDS practices.

  PublisherDOI

• Analyzing the Shifts in Users Data Focus in Exploratory Visual Analysis

  2025-03-19 · 1 citations

  articleOpen access
  PublisherDOI

• How Do Observable Users Decompose D3 Code? A Qualitative Study

  2025-11-01

  articleSenior author

  Many toolkit developers seek to streamline the visualization programming process through structured support such as prescribed templates and example galleries. However, few projects examine how users organize their own visualization programs and how their coding choices may deviate from the intents of toolkit developers, impacting visualization prototyping and design. Further, is it possible to infer users’ reasoning indirectly through their code, even when users copy code from other sources? We explore this question through a qualitative analysis of 715 D3 programs on Observable. We identify three levels of program organization based on how users decompose their code into smaller blocks: Program-, Chart-, and Component-Level code decomposition, with a strong preference for Component-Level reasoning. In a series of interviews, we corroborate that these levels reflect how Observable users reason about visualization programs. We compare common usermade components with those theorized in the Grammar of Graphics to assess overlap in user and toolkit developer reasoning. We find that, while the Grammar of Graphics covers basic visualizations well, it falls short in describing complex visualization types, especially those with animation, interaction, and parameterization components. Our findings highlight how user practices differ from formal grammars and reinforce ongoing efforts to rethink visualization toolkit support, including augmenting learning tools and AI assistants to better reflect real-world coding strategies.

  PublisherDOI

• African Data Ethics: A Discursive Framework for Black Decolonial AI

  2025-06-23 · 7 citations

  articleOpen accessSenior author
  PublisherDOI

• A Design Space of Behavior Change Interventions for Responsible Data Science

  2025-03-19 · 3 citations

  articleOpen access
  PublisherDOI

• Behavior Matters: An Alternative Perspective on Promoting Responsible Data Science

  Proceedings of the ACM on Human-Computer Interaction · 2025-05-02 · 2 citations

  article

  Data science pipelines inform and influence many daily decisions, from what we buy to who we work for and even where we live. When designed incorrectly, these pipelines can easily propagate social inequity and harm. Traditional solutions are technical in nature; e.g., mitigating biased algorithms. In this vision paper, we introduce a novel lens for promoting responsible data science using theories of behavior change that emphasize not only technical solutions but also the behavioral responsibility of practitioners. By integrating behavior change theories from cognitive psychology with data science workflow knowledge and ethics guidelines, we present a new perspective on responsible data science. We present example data science interventions in machine learning and visual data analysis, contextualized in behavior change theories that could be implemented to interrupt and redirect potentially suboptimal or negligent practices while reinforcing ethically conscious behaviors. We conclude with a call to action to our community to explore this new research area of behavior change interventions for responsible data science.

  PublisherDOI

• A Crowdsourced Study of ChatBot Influence in Value-Driven Decision Making Scenarios

  ArXiv.org · 2025-11-19

  preprintOpen accessSenior author

  Similar to social media bots that shape public opinion, healthcare and financial decisions, LLM-based ChatBots like ChatGPT can persuade users to alter their behavior. Unlike prior work that persuades via overt-partisan bias or misinformation, we test whether framing alone suffices. We conducted a crowdsourced study, where 336 participants interacted with a neutral or one of two value-framed ChatBots while deciding to alter US defense spending. In this single policy domain with controlled content, participants exposed to value-framed ChatBots significantly changed their budget choices relative to the neutral control. When the frame misaligned with their values, some participants reinforced their original preference, revealing a potentially replicable backfire effect, originally considered rare in the literature. These findings suggest that value-framing alone lowers the barrier for manipulative uses of LLMs, revealing risks distinct from overt bias or misinformation, and clarifying risks to countering misinformation.

  PublisherOA PDFDOI

• African Data Ethics: A Discursive Framework for Black Decolonial Data Science

  ArXiv.org · 2025-02-22 · 1 citations

  preprintOpen accessSenior author

  The shift towards pluralism in global data ethics acknowledges the importance of including perspectives from the Global Majority to develop responsible data science practices that mitigate systemic harms in the current data science ecosystem. Sub-Saharan African (SSA) practitioners, in particular, are disseminating progressive data ethics principles and best practices for identifying and navigating anti-blackness and data colonialism. To center SSA voices in the global data ethics discourse, we present a framework for African data ethics informed by the thematic analysis of an interdisciplinary corpus of 50 documents. Our framework features six major principles: 1) Challenge Power Asymmetries, 2) Assert Data Self-Determination, 3) Invest in Local Data Institutions & Infrastructures, 4) Utilize Communalist Practices, 5) Center Communities on the Margins, and 6) Uphold Common Good. We compare our framework to seven particularist data ethics frameworks to find similar conceptual coverage but diverging interpretations of shared values. Finally, we discuss how African data ethics demonstrates the operational value of data ethics frameworks. Our framework highlights Sub-Saharan Africa as a pivotal site of responsible data science by promoting the practice of communalism, self-determination, and cultural preservation.

  PublisherOA PDFDOI

• An Adaptive Benchmark for Modeling User Exploration of Large Datasets

  Proceedings of the ACM on Management of Data · 2025-02-10 · 1 citations

  articleSenior author

  In this paper, we present a new DBMS performance benchmark that can simulate user exploration with any specified dashboard design made of standard visualization and interaction components. The distinguishing feature of our SImulation-BAsed (or SIMBA) benchmark is its ability to model user analysis goals as a set of SQL queries to be generated through a valid sequence of user interactions, as well as measure the completion of analysis goals by testing for equivalence between the user's previous queries and their goal queries. In this way, the SIMBA benchmark can simulate how an analyst opportunistically searches for interesting insights at the beginning of an exploration session and eventually hones in on specific goals towards the end. To demonstrate the versatility of the SIMBA benchmark, we use it to test the performance of four DBMSs with six different dashboard specifications and compare our results with IDEBench. Our results show how goal-driven simulation can reveal gaps in DBMS performance missed by existing benchmarking methods and across a range of data exploration scenarios.

  PublisherDOI

• Considering Visualization Example Galleries

  arXiv (Cornell University) · 2024-07-30

  preprintOpen accessSenior author

  Example galleries are often used to teach, document, and advertise visually-focused domain-specific languages and libraries, such as those producing visualizations, diagrams, or webpages. Despite their ubiquity, there is no consensus on the role of "example galleries", let alone what the best practices might be for their creation or curation. To understand gallery meaning and usage, we interviewed the creators (N=11) and users (N=9) of prominent visualization-adjacent tools. From these interviews we synthesized strategies and challenges for gallery curation and management (e.g. weighing the costs/benefits of adding new examples and trade-offs in richness vs ease of use), highlighted the differences between planned and actual gallery usage (e.g. opportunistic reuse vs search-engine optimization), and reflected on parts of the gallery design space not explored (e.g. highlighting the potential of tool assistance). We found that galleries are multi-faceted structures whose form and content are motivated to accommodate different usages--ranging from marketing material to test suite to extended documentation. This work offers a foundation for future support tools by characterizing gallery design and management, as well as by highlighting challenges and opportunities in the space (such as how more diverse galleries make reuse tasks simpler, but complicate upkeep).

  PublisherOA PDFDOI

Recent grants

• CRII: CHS: Modeling Analysis Behavior to Support Interactive Exploration of Massive Datasets

  NSF · $175k · 2019–2021

• CAREER: Behavior-Driven Testing of Big Data Exploration Tools

  NSF · $607k · 2022–2027

Frequent coauthors

• Michael Stonebraker

  Massachusetts Institute of Technology

  12 shared

• Remco Chang

  11 shared

• Zehua Zeng

  9 shared

• Junran Yang

  Seattle University

  9 shared

• Jeffrey Heer

  University of Washington

  9 shared

• Hannah K. Bako

  7 shared

• Alvitta Ottley

  7 shared

• Michael Correll

  Northeastern University

  7 shared

Education

• B.S., Computer Engineering

  University of Washington

  2011

• M.S., Computer Science

  Massachusetts Institute of Technology

  2013

• Ph.D., Computer Science

  Massachusetts Institute of Technology

  2017

Awards & honors

• 2022 TCDE Rising Star Award
• 2023 IEEE VGTC Significant New Researcher Award
• Named one of the 35 Innovators Under 35 by the MIT Technolog…
• Sloan Fellow in 2023