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Andrew J. King 0002

dblp:43/3878-2 · also Andrew Joseph King · DBLP profile ↗
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7ranked-venue papers
5as first author
3since 2021 · last 2026
0000-0002-9809-0563ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 5 · 5 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
2 papers
Human-AI interaction · 59% Health and well-being technologies · 34% Design research and methods · 8%
Artificial intelligence
1 paper
Trustworthy machine learning · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-AI interaction
AI-assisted decision-making
1.012026
Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions · CHI 2026
Health and well-being technologies › health informatics
clinical decision support
1.012026
Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions · CHI 2026
Machine learning › Trustworthy machine learning
interpretability
0.312026
Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions · CHI 2026
Medical and health informatics
intensive care unit
0.212024
Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit · CHI 2024
Design research and methods
design workshops
0.212024
Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit · CHI 2024

Methods — techniques the papers use, named apart from their topics

think-aloud study · 2.0contextual inquiry · 2.0first-hand account · 1.5design workshop · 1.5
YearPublicationVenuePosition
2026 Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions
abstract
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 underneath. We address this gap by reconsidering AI interfaces as collections of intelligent reasoning cues: discrete pieces of AI information that can individually influence decision-making. We then explore the roles of eight types of reasoning cues in a high-stakes clinical decision (treating patients with sepsis in intensive care). Through contextual inquiries with six teams and a think-aloud study with 25 physicians, we find that reasoning cues have distinct patterns of influence that can directly inform design. Our results also suggest that reasoning cues should prioritize tasks with high variability and discretion, adapt to ensure compatibility with evolving decision needs, and provide complementary, rigorous insights on complex cases.
Venkatesh Sivaraman, Eric Paul Mason, Mengfan Ellen Li, Jessica Tong, Andrew J. King 0002, Jeremy M. Kahn, Adam Perer
CHI5
2024 Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit
abstract
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 point. How might multidisciplinary teams identify buildable and desirable use cases? This paper presents a first hand account of ideating AI concepts to improve critical care medicine. As a team of data scientists, clinicians, and HCI researchers, we conducted a series of design workshops to explore more effective approaches to AI concept ideation and problem formulation. We detail our process, the challenges we encountered, and practices and artifacts that proved effective. We discuss the research implications for improved collaboration and stakeholder engagement, and discuss the role HCI might play in reducing the high failure rate experienced in AI innovation.
Nur Yildirim, Susanna Zlotnikov, Deniz Sayar, Jeremy M. Kahn, Leigh A. Bukowski, Sher Shah Amin, Kathryn A. Riman, Billie S. Davis, John S. Minturn, Andrew J. King 0002, Dan Ricketts, Lu Tang 0003, Venkatesh Sivaraman, Adam Perer, Sarah Masud Preum, James McCann, John Zimmerman
CHI10
2023 A voice-based digital assistant for intelligent prompting of evidence-based practices during ICU rounds
Andrew J. King 0002, Derek C. Angus, Gregory F. Cooper, Danielle L. Mowery, Jennifer B. Seaman, Kelly M. Potter, Leigh A. Bukowski, Ali Al-Khafaji, Scott R. Gunn, Jeremy M. Kahn
J. Biomed. Informatics1
2019 Insights from a dissertation on the development of a Learning Electronic Medical Record System: data-driven, context-aware learning
Andrew J. King 0002, Shyam Visweswaran, Harry Hochheiser, Gilles Clermont, Gregory F. Cooper
AMIA1
2019 Using machine learning to selectively highlight patient information
Andrew J. King 0002, Gregory F. Cooper, Gilles Clermont, Harry Hochheiser, Milos Hauskrecht, Dean F. Sittig, Shyam Visweswaran
J. Biomed. Informatics1
2018 Using Machine Learning to Predict the Information Seeking Behavior of Clinicians Using an Electronic Medical Record System
Andrew J. King 0002, Gregory F. Cooper, Harry Hochheiser, Gilles Clermont, Milos Hauskrecht, Shyam Visweswaran
AMIA1
2015 Development and Preliminary Evaluation of a Prototype of a Learning Electronic Medical Record System
Andrew J. King 0002, Gregory F. Cooper, Harry Hochheiser, Gilles Clermont, Shyam Visweswaran
AMIA1