Finding the Right Recipe
Walking the same hallways he once traversed as a student, Cam Kormylo (BBA ’19) now finds himself on the other side of the desk, teaching analytics at the University of Notre Dame’s Mendoza College of Business.
- Published
Dungeon Tacos in Dunne Hall
Sometimes, Kormylo still does a double take when he unlocks the office door with his name written on it. As a kid, he never imagined attending a traditional four-year college, much less becoming an academic. In fact, it seemed obvious that he'd go to culinary school and become a chef, and ultimately own his own restaurant. While classmates in his small town in Wisconsin tuned into Power Rangers, he visited his Italian great-grandmother's couch to binge-watch celebrity chef Emeril Lagasse prepare delicacies like andouille-crusted redfish. Every year for his birthday, Kormylo's godparents would drive him into Chicago and treat him to dinner at whatever restaurant was causing the loudest buzz on the famed Chicago Loop's food scene. When Kormylo turned 14, that was Girl & the Goat, chef Stephanie Izard's hotspot that permanently disrupted the Windy City’s Restaurant Row by introducing global flavors to fine dining palettes. "When you're a kid dreaming of becoming a chef, people think it's cool, and they are receptive," Kormylo said. "The waiter took me back to the kitchen, where I met Stephanie. One thing led to another and she invited me to help in the kitchen for a few days."
Breaking research on betrayal aversion with AI
It's been a big year for Kormylo. In addition to launching his academic career and making the permanent move back to South Bend, he published his first research paper in Management Science: "Till Tech Do Us Part: Betrayal Aversion and its Role in Algorithm Use." His co-authors are Idris Adjerid of Pamplin College of Business, Sheryl B. Ball of Virginia Tech, and Can Dogan of Radford University. The paper compares people's decision-making processes when offered expert advice from human experts versus AI-powered algorithmic experts. Kormylo and his colleagues drew on decades of research in betrayal aversion, which examines how individuals respond when offered advice from an expert (such as a financial advisor). He said, "If your advisor wrongs you, there will be two costs: you'll lose money, but there will also be the emotional cost of having been wronged by someone you trusted." The paper's purpose was to investigate how much this effect persists as human experts are replaced by AI algorithmic experts, particularly in financial trading. Kormylo spent a year designing a financial market simulator game and then ran experiments on Amazon Mechanical Turk, a crowdsourcing marketplace that hires workers for tasks like participating in online surveys. The 300 participants were told they could earn $20-$30 (with some earning as much as $75) by playing 40 rounds of a financial trading game. In each round, they were offered the choice to either make their own decision about how much of their endowment to invest in a risky trade or to defer to a human or algorithmic expert's advice. They were informed that the "expert" may make a choice out of self-interest that won't be in the participant's best interest. Kormylo expected that "people are still going to feel betrayed by algorithms even if they can't by definition intend to betray you." In reality, the results were the opposite. When a human advisor helped the individuals and introduced a misalignment, it decreased participants' future usage of the expert by 16%. However, participants didn't change their future choices when an algorithmic advisor exhibited the same misstep. Participants continued leaning on the algorithm and ended up earning 20% more than the other group purely by placing their trust in the virtual advisor. Kormylo said the implications for the financial services industry are clear: While banks and other institutions often assume that anthropomorphizing an algorithm with a face and a name will increase clients’ trust, they may be more forgiving of choices with poor consequences produced by a non-human algorithm. "There is a phenomenon of organizations trying to hide behind their algorithm's mistakes. This research doesn't imply that, ultimately, humans aren't responsible for their firm's technology," Kormylo clarified. He and colleagues Margaret Traeger, Tim Hubbard, and Mike Villano are conducting similar work to examine how team dynamics shift when working with algorithms that have been assigned male or female voices and names.Auditing AI
As a teacher, Kormylo aims to teach students how to evaluate and assess AI systems from inside organizations and companies. "We approach the class with the belief that organizations are heavily incentivized to ensure that models they put out are relevant and effectively solve problems,” he explained. “But just as importantly, they have to look at the ethical implications, fairness and data privacy." In the first half of the semester, students learn to use tools such as explainability techniques, fairness metrics, interviews and ethnography. Next, they apply their toolkit to case studies. Because AI is rapidly changing, case studies are, too. This is the third time that Kormylo has taught the class, and the case studies have changed each semester to keep up with developments in the field. "AI is a tool that organizations are going to expect Mendoza alums to use, whether we like it or not. It's our job to prepare students for the workforce," Kormylo said. To that end, he has ChatGPT set up in class and shows students how to take advantage of its support, but also what can go wrong if a user isn't feeding it the right prompts or double-checking its outputs. And then, after a day at Mendoza, Kormylo comes home and cooks dinner for his family of four. Tacos are often on the menu.Related News & Stories

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