Each week, Daniels is featuring a researcher who conducts meaningful research that impacts their field and the wider community. Learn more about their work in Q&As with the Daniels Research team and email them to nominate yourself or a colleague for a future Q&A.
Lily Morse (PhD Carnegie Mellon University) is an assistant professor of management at the Daniels College of Business. Before joining Daniels in 2024, she was a faculty member at West Virginia University. She has also served as a visiting assistant professor at Boston College and postdoctoral associate at the University of Notre Dame.
Lily’s research focuses on pressing ethical issues faced by organizations, including how to manage AI responsibly. She explores the gap between how technology performs and how fairly it’s perceived–and how managers can bridge that gap. Her work provides both theoretical insights and practical guidance for navigating the ethical complexities of the Fourth Industrial Revolution. Her research has been published in leading journals including Organizational Behavior and Human Decision Processes, MIS Quarterly, Nature Machine Intelligence, Journal of Business Ethics, and Journal of Personality and Social Psychology.
What do you study? How did you become interested in the field?
I earned my undergraduate degree in psychology where I became fascinated by ethical issues—especially how people handle conflicts of interest. In graduate school at Carnegie Mellon, I studied organizational behavior and theory and my early research looked at the dark side of ethics: what happens when people cross ethical boundaries. At Carnegie Mellon, I was surrounded by computer scientists and I kept hearing them talk about building technologies that had never been existed before. What I wasn’t hearing were conversations about ethics. That tension between rapid technological innovation and ethical responsibility drew me in. I began to study how people perceive and experience ethics when working with cutting-edge technologies, especially machine learning models. Since then, my work has focused on how we can manage AI responsibly. I’m interested in person-centered approaches—ways of designing and using tools so that employees, consumers and other stakeholders feel their interactions with both the company and technology are ethical and fair.
How do you study it?
The only way to study this topic is through interdisciplinary collaboration. I often work with information science scholars, computer scientists and other management researchers. Together, we brainstorm conceptual projects and write theory papers, but I also do empirical research. Many of my empirical studies use multi-method designs, mixing archival data with experiments that allow us to uncover mechanisms of behavior. Because this is such a broad and fast-moving field, triangulating across methods helps to address the complexity of the questions I’m asking. One recent project, currently under revise-and-resubmit at the Journal of Business Ethics, looks at negotiations with AI chatbots. We found that even when chatbots are programmed to act fairly, people change their behavior simply because their counterpart isn’t human. Human negotiators became more self-interested and more willing to lie to chatbots. This finding shows that ethical challenges don’t just come from technical design flaws—they also arise from the way humans adapt to technology. Even if a system is designed to be fair, the human response can create new ethical risks.
What’s next in your research?
Right now, I’m pursuing two streams of AI research. The first focuses on hiring. Many organizations use machine learning algorithms in candidate screening and selection, and I want to understand how applicants react when they learn algorithms are being used. I’m especially interested in how perceptions of fairness influence people’s decisions to apply—or not apply—for a job. The second project, in collaboration with colleagues from multiple disciplines, examines how companies frame AI adoption in their public communications. We’re looking at how that “AI talk” shapes stakeholder perceptions of the firm and raises broader ethical questions about responsibility and transparency.
How do you bring this work into the classroom?
AI is already in the classroom—students bring it in themselves. My role is to guide how it’s used. I start by building awareness and reflection: What tools are students using? What are the implications? I worry that students might cheat themselves by relying on technology and missing out on developing core skills. So, I focus on helping them see technology as a supplement, not a substitute. We use activities like Tarot Cards of Tech, where students imagine best- and worst-case scenarios and think about who might be harmed. We also run simulations where students can quickly see how AI systems become biased—whether through chatbots or deep learning architectures. I’m especially excited about teaching a new Ethics and AI course, which will give students the chance to grapple with these questions in depth.
What impact do you hope your research has on society?
I hope my research encourages organizations to take a breath before rushing to adopt new technologies. Too often, startups and engineers follow the “move fast and break things” mentality—breaking rules until forced to stop. I don’t think that works when people’s lives and well-being are at stake. My goal is to help organizations adopt AI tools in a person-centered way, with processes that anticipate ethical failures before they happen. If managers can use my research to slow down, think ahead and prevent harm, then I believe we can create morally conscious organizations that are better for both people and society.