Information Science · Human–AI Decision Making
How people use evidence.
How decisions become open to question.
I’m Kerry Thornhill, an MS student and researcher at the University of North Texas. I study how people obtain, interpret, and use information when evaluating AI-supported institutional decisions—and what makes a specific concern possible to investigate.
University of North Texas
Anuradha and Vikas Sinha College of Artificial Intelligence and Advanced Analytics
MS in Interdisciplinary Studies · In progress
Research in Information Science
Experience that became
a research direction.
My path into research grew from responsibility for decisions with practical consequences.
In real estate, transportation, and organizational information systems, I worked where records, rules, financial stakes, and human judgment meet.
That experience sharpened questions I now pursue academically: What information supports a judgment? How can someone investigate a mistaken record or an unsuitable criterion? What allows relevant evidence to change an assessment?
I am building a sustained academic research career around these questions. Graduate study connects my practical experience with conceptual analysis and the development of empirical methods for studying information use and human–AI judgment.
The experience behind the questionsAn explanation is
only a beginning.
My research asks what makes a consequential decision open to examination—and what allows independent evidence to remain in play.
Information for meaningful challenge
What information helps a person recognize and investigate a specific concern about an institutional decision?
02Evidence use around AI
When do people retain valid independent evidence, and when does advice change how that evidence is weighted?
03Institutional answerability
Under what conditions does an institution owe an informed challenge a consequential response?
03 / Experience with direction
A practical foundation.
A scholarly purpose.
I have led distributed teams, operated across regulatory environments, and learned to build the digital systems those teams depend on. Returning to scholarship brought a new purpose to that preparation: turning difficult practical questions into disciplined, testable inquiry.
I’m pursuing a Master of Science in Interdisciplinary Studies at the University of North Texas. My research in Information Science brings philosophical analysis and systems experience to the study of evidence, human–AI judgment, and algorithmic contestability.
Follow the trajectory04 / What the work is for
Research others can
examine and use.
I plan to make the research useful through accessible explanations, reusable synthetic cases, documented methods, and teaching materials.
The aim is to help researchers, students, and people who encounter AI-supported decisions examine evidence and ask better questions.
The public outlines and worked example on Contestability are early steps in that direction. Further research materials and educational resources will be added as the work develops.
Explore the worked example