Courses a.y. 2024/2025
Biographical note
I am a social scientist and I conduct interdisciplinary, empirical research that investigates adoption of artificial intelligence as a tool for social good, with direct applications for companies, law, and policy. A secondary area of research looks at the determinants of positive behavior change and the drivers of sustainability and climate action. I completed a Ph.D. in marketing at New York University’s Stern School of Business. I also hold a M.S. (summa cum laude) from Bocconi University, a M.A. (Honors) in Psychology from New York University, and a M. Phil. in Marketing from New York University’s Stern School of Business.
Research interests
My primary area of research falls under the realm of investigating consumer psychological responses to applications of artificial intelligence across domains spanning healthcare, recommendation systems, automated content generation, and government service provision. A secondary area of research broadly relates to consumer and societal well-being. My work in this area looks at the determinants of positive behavior change and the drivers of sustainability and climate action.
Working papers
Plagiarizing AI-generated content is seen as less unethical and more permissible
Knowledge of artificial intelligence predicts lower AI receptivity
Selected Publications
AI-induced indifference: unfair AI reduces prosociality
COGNITION, 2024
PNAS NEXUS, 2024
SCIENCE ADVANCES, 2024
Proximity bias: Interactive effect of spatial distance and outcome valence on probability judgments
JOURNAL OF CONSUMER PSYCHOLOGY, Forthcoming
Algorithmic transference: people overgeneralize failures of AI in the government
JOURNAL OF MARKETING RESEARCH, 2023
Artificial Intelligence in utilitarian vs. hedonic contexts: the “word-of-machine” effect
JOURNAL OF MARKETING, 2022
News from generative artificial intelligence is believed less
Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 2022
Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning
PNAS NEXUS, 2022
How consumer digital signals are reshaping the customer journey
JOURNAL OF THE ACADEMY OF MARKETING SCIENCE, 2022
National identity predicts public health support during a global pandemic: Results from 67 countries
NATURE COMMUNICATIONS, 2022
Understanding, explaining, and utilizing medical artificial intelligence
NATURE HUMAN BEHAVIOUR, 2021
Resistance to medical artificial intelligence is an attribute in a compensatory decision process: Response to Pezzo and Becksted
JUDGMENT AND DECISION MAKING, 2020
Advertising a Desired Change: When Process Simulation Fosters (vs. Hinders) Credibility and Persuasion
JOURNAL OF MARKETING RESEARCH, 2020
Resistance to medical Artificial Intelligence
THE JOURNAL OF CONSUMER RESEARCH, 2019