Emily Wall

schoolEmory Universitybar_chart#1468 Global Rank

Total Papers

HCI Rank

#1468

Research Areas

Years Active

Recent Publications

Does a Picture Paint a Thousand Words? Using Visual and Textual Channels to Understand Attitudes and Beliefs

In Human-Computer Interaction, eliciting user attitudes and beliefs is crucial for understanding user interactions with technology. Existing elicitation methods range from expressive open-ended text to structured formats like Likert scales. Expressive methods yield rich insights but are difficult to systematically ana…

CHI '26CorrespondingAI Ethics, Fairness & Accountability +2

Evaluating Behavior Change Interventions for Responsible Data Science

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 evalu…

CHI '26CorrespondingQuantified Self +2

Spotify Warped: Reshaping Personal Informatics via Music Listening, Casual Users, Passive Data and Episodic Reflection

Analysing personal datasets has traditionally been limited to `Quantified Selfers' who commit significant effort into manually recording and analysing their data. However, the pool of Casual Users (CUs) who \textit{can} engage with their personal data is increasing due to the prevalence of companies passively collecti…

CSCW '25CorrespondingResponsible and Ethical AI

Behavior Matters: An Alternative Perspective on Promoting Responsible Data Science

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…

IUI '25CorrespondingAI Ethics, Fairness & Accountability +1

A Design Space of Behavior Change Interventions for Responsible Data Science

Behavior change theories, rooted in psychology and sociology, offer valuable insights into why and how individuals and groups modify their actions and decisions. By leveraging these theories in the context of responsible data science, we can better understand and influence the behaviors of data scientists, who play a…

CHI '25CorrespondingInteractive Data Visualization +2

Confirmation Bias: The Double-Edged Sword of Data Facts in Visual Data Communication

Incorporating data facts, which are natural language descriptions of data patterns, alongside visualizations can guide readers and enhance the visibility of data patterns. However, data facts might also induce confirmation bias in visual analysis. We conducted a series of crowdsourced experiments to explore the biasin…

CHI '25CorrespondingInteractive Data Visualization +1

A Novel Lens on Metacognition in Visualization

Metacognition, or the awareness and regulation of one's own cognitive processes, allows individuals to take command of their learning and decision making in various contexts. In tasks that require problem-solving and adaptive learning, individuals with heightened metacognitive awareness tend to outperform others, as…

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