Carol J. Smith
Software Engineering Institute
Carol Smith is a senior research scientist in human-machine interaction in the SEI AI Division. In this role Smith contributes to research and development focused on improving user experiences (UX) and interactions with the nation’s AI systems, robotics, and other complex and emerging technologies. Smith’s research includes human-computer interaction (HCI), cognitive psychology, ethics, and human-machine teaming. Smith is also an adjunct instructor for Carnegie Mellon University’s Human-Computer Interaction Institute where she has taught both bachelor and master’s level courses. Smith has been conducting research to improve the human experience with complex systems across industries for over 20 years. Smith has been researching and advocating for AI ethics and responsible approaches for human-machine teaming and experiences with AI systems, autonomous vehicles, and other emerging technologies since 2015. Smith has been an active volunteer in technical communities and was Vice President of the User Experience Professionals Association (UXPA) and served two terms on the international board. Smith is currently an ACM Distinguished Speaker, an IEEE working group member for the P7008 Standard, and an editor for the UXPA Journal of Usability Studies. Smith holds a master’s degree in HCI from DePaul University.
Publications by Carol J. Smith
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Exploring an AI Engineering Body of Knowledge
September 21, 2022 • Webinar
Carol J. SmithMichael Mattarock
In this webcast, Carol Smith, Carrie Gardner, and Michael Mattarock discuss maturing artificial intelligence (AI) practices based on the current body of knowledge from the AI Division.
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Trust and AI Systems
August 11, 2022 • Podcast
Carol J. SmithDustin D. Updyke
Carol Smith, a senior research scientist in human machine interaction, and Dustin Updyke, a senior cybersecurity engineering in the SEI’s CERT Division, discuss the construction of trustworthy AI systems and factors influencing human trust of AI systems.
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Exploring Opportunities in Usable Hazard Analysis Processes for AI Engineering
March 21, 2022 • Conference Paper
Nikolas Martelaro (Carnegie Mellon University)Carol J. SmithTamara Zilovic (Carnegie Mellon University)
This paper was presented at the 2022 AAAI Spring Symposium on AI Engineering.
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Implementing the DoD's Ethical AI Principles
January 13, 2022 • Podcast
Alexandrea Van DeusenCarol J. Smith
In this SEI podcast, Alex Van Deusen and Carol Smith, both with the SEI's AI Division, discuss a recent project in which they helped the Defense Innovation Unit of the U.S. Department of Defense to develop guidelines for the responsible use of AI.
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Bias in AI: Impact, Challenges, and Opportunities
September 30, 2021 • Podcast
Carol J. SmithJonathan Spring
Carol Smith discusses with Jonathan Spring the hidden sources of bias in artificial intelligence (AI) systems and how systems developers can raise their awareness of bias, mitigate consequences, and reduce risks.
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Human-Centered AI
June 25, 2021 • White Paper
Hollen BarmerRachel DzombakMatt Gaston
This white paper discusses Human-Centered AI: systems that are designed to work with, and for, people.
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Is Your Organization Ready for AI?
June 24, 2021 • Podcast
Carol J. SmithRachel Dzombak
Digital transformation lead Dr. Rachel Dzombak and research scientist Carol Smith discuss how AI Engineering can support organizations to implement AI systems.
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My Story in Computing with Carol Smith
April 15, 2021 • Podcast
Carol J. Smith
Carol Smith, who trained as a photojournalist, transitioned a love of telling people's stories to a career in human-computer interaction working in artificial intelligence with the SEI's Emerging Technology Center.
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Ethics in AI Engineering
December 15, 2020 • Video
Carol J Smith
The presentation discusses how to reduce unintended/harmful bias and prevent the inevitable harm that comes from "unknowable" systems.
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Ethics in AI
November 03, 2020 • Presentation
Carol J Smith
The presentation discusses how to reduce unintended/harmful bias and prevent the inevitable harm that comes from "unknowable" systems.
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Designing Trustworthy AI
April 29, 2020 • Podcast
Carol J Smith
Carol Smith discusses a framework that builds upon the importance of diverse teams and ethical standards to ensure that AI systems are trustworthy and able to effectively augment warfighters.
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Implementing Ethics: Developing Trustworthy AI at PyCon 2020
April 28, 2020 • Presentation
Carol J Smith
This presentation introduces the topic of ethics and walks through a user experience (UX) framework to guide AI development teams successfully through this process.
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Designing Trustworthy AI: A User Experience (UX) Framework
March 24, 2020 • Webinar
Carol J Smith
This webcast introduced a new user experience (UX) framework to guide the creation of AI systems that are accountable, de-risked, respectful, secure, honest and usable.
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Human–Machine Teaming and AI
January 29, 2020 • Video
Carol J SmithAndrew O. MellingerRitwik Gupta
In this Cyber Talk, SEI researchers Carol Smith, Andrew Mellinger, and Ritwik Gupta discuss the exciting challenges and opportunities for human–machine teaming and artificial intelligence.
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Designing Trustworthy AI: A Human-Machine Teaming Framework to Guide Development
December 10, 2019 • Conference Paper
Carol J Smith
The Human-Machine Teaming (HMT) Framework for Designing Ethical AI Experiences, when used with a set of technical ethics, will guide AI development teams to create AI systems that are accountable, de-risked, respectful, secure, honest, and usable
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