Venkatesh Sivaraman

schoolCarnegie Mellon Universitybar_chart#994 Global Rank

Total Papers

HCI Rank

#994

Research Areas

Years Active

Recent Publications

Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions

Artificial intelligence (AI)-based decision support systems can be highly accurate yet still fail to support users or improve decisions. Existing theories of AI-assisted decision-making focus on calibrating reliance on AI advice, leaving it unclear how different system designs might influence the reasoning processes u…

CSCW '251st AuthorIdentifying and Mitigating AI Risks

Static Algorithm, Evolving Epidemic: Understanding the Potential of Human-AI Risk Assessment to Support Regional Overdose Prevention

Drug overdose deaths, including those due to prescription opioids, represent a critical public health issue in the United States and worldwide. Artificial intelligence (AI) approaches have been developed and deployed to help prescribers assess a patient's risk for overdose-related death, but it is unknown whether publ…

CHI '251st AuthorIdentity & Avatars in XR +2

Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks

Temporal predictive models have the potential to improve decisions in health care, public services, and other domains, yet they often fail to effectively support decision-makers. Prior literature shows that many misalignments between model behavior and decision-makers' expectations stem from issues of model specificat…

CHI '251st AuthorInteractive Data Visualization +1

Divisi: Interactive Search and Visualization for Scalable Exploratory Subgroup Analysis

Analyzing data subgroups is a common data science task to build intuition about a dataset and identify areas to improve model performance. However, subgroup analysis is prohibitively difficult in datasets with many features, and existing tools limit unexpected discoveries by relying on user-defined or static subgroups…

CHI '245th+ AuthorGenerative AI (Text, Image, Music, Video) +2

Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit

Advances in artificial intelligence (AI) have enabled unprecedented capabilities, yet innovation teams struggle when envisioning AI concepts. Data science teams think of innovations users do not want, while domain experts think of innovations that cannot be built. A lack of effective ideation seems to be a breakdown p…

CHI '231st AuthorExplainable AI (XAI) +1

Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care

Artificial intelligence (AI) in healthcare has the potential to improve patient outcomes, but clinician acceptance remains a critical barrier. We developed a novel decision support interface that provides interpretable treatment recommendations for sepsis, a life-threatening condition in which decisional uncertainty i…

IUI '221st AuthorInteractive Data Visualization +1

Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spaces

Modern machine learning techniques commonly rely on complex, high-dimensional embedding representations to capture underlying structure in the data and improve performance. In order to characterize model flaws and choose a desirable representation, model builders often need to compare across multiple embedding spaces,…

CHI '222nd AuthorHonorable MentionAI-Assisted Decision-Making & Automation +1

Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support

AI-based decision support tools (ADS) are increasingly used to augment human decision-making in high-stakes, social contexts. As public sector agencies begin to adopt ADS, it is critical that we understand workers’ experiences with these systems in practice. In this paper, we present findings from a series of intervie…

CHI '224th AuthorAI-Assisted Decision-Making & Automation +1

How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions

Machine learning tools have been deployed in various contexts to support human decision-making, in the hope that human-algorithm collaboration can improve decision quality. However, the question of whether such collaborations reduce or exacerbate biases in decision-making remains underexplored. In this work, we conduc…

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