LaCross AI Institute
Research - LaCross AI Institute
Research
The LaCross Institute is focused on advancing interdisciplinary research at the forefront of innovation in ethical artificial intelligence and its application in business.
Artificial Intelligence draws on many disciplines, including philosophy, mathematics, economics, psychology, linguistics, neuroscience and others, necessitating an interdisciplinary approach to advancing our understanding and application of AI including in business. This requires that we understand the ethical implications of its use, shape how leaders and organizations think about its role in their businesses, and establish ways of managing human and AI that consider the impacts on all stakeholders. The LaCross Institute supports research across the breadth of AI topics by researchers from a wide range of disciplines, who share our desire to push the boundaries of artificial intelligence for the greater good.
-
Focus Areas
Our research focuses on “the intersection of data science and business” in light of the rapid innovation in data and technology taking place in the business world.
However, the white space that exists at this intersection is unmanageably large and offers little guidance in how and where to deploy the limited resources available to us. As a result, and guided by the mission described above, we defined six areas of focus within this white space that align with priorities previously identified by Darden, the School of Data Science, and the University of Virginia. We call them “themes,” and they are as follows:
- Bias and Misinformation. Exploring algorithms and data-intensive business practices that increase equity and promote truthfulness in business and in society.
- Analytical Leadership. Managing and leading analytical individuals, high-performing teams, and distinctive organizations in the face of an explosion of data and the near ubiquity of technologies that enable leaders to use or misuse it.
- Healthy Choices. Understanding and influencing consumer and health care professional decision-making behavior through interventions, experiments, and analysis using data and technology, with the objective of improving health and better managing care.
- Low/No-code. Expanding the accessibility of data science tools and methods beyond the most sophisticated practitioners to enable application of data science to difficult challenges in business by a broader range of individuals, teams, and organizations.
- Entrepreneurship. Developing the data science skills of entrepreneurs and advancing the translation of innovations in data science into commercial businesses, in Charlottesville and across the region.
- Social Impact. Enhancing the availability of data and data science to leaders in organizations, such as local and regional governments and nonprofits, that are focused on issues and opportunities to positively impact communities and society.
-
Featured AI Research
Academic Research
Longoni, C., Cian, L., Kyung, E. J. "Algorithmic Transference: People Overgeneralize Failures of AI in the Government." Journal of Marketing Research, 60(1), 170–188. 2023.
Guo, X., Grushka-Cockayne, Y., De Reyck, B. "Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning." Manufacturing & Service Operations Management, 24(6), 2797-3306. 2022.
Raveendhran, R., Kim-Schmid, J. "Where AI Can — and Can’t — Help Talent Management." Harvard Business Review. 2022.
Other Publications
Albert, Michael. "Learning in Online Principal-Agent Interactions: The Power of Menus." Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-24), Vancouver, Canada, 2024.
Reinartz, Werner, and Venkatesan, Rajkumar. (2022), “A Better Way to Calculate Marketing ROI,” Harvard Business Review, 108-110.
Korinek, A., & Suh, D. (2024). Scenarios for the Transition to AGI (No. w32255). National Bureau of Economic Research. https://doi.org/10.3386/w32255
-
AI Research Archive
Longoni, C., Cian, L., Kyung, E. J. "Algorithmic Transference: People Overgeneralize Failures of AI in the Government." Journal of Marketing Research, 60(1), 170–188. 2023.
Tomislav, P., Cian, L., Franc, R., et al. "Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning." PNAS Nexus, 1(3), 1-15. 2022.
Longoni, C., Cian, L. "Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The 'Word-of-Machine' Effect." Journal of Marketing, 86(1), 91-108. 2022.
Guo, X., Grushka-Cockayne, Y., De Reyck, B. "Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning." Manufacturing & Service Operations Management, 24(6), 2797-3306. 2022.
Raveendhran, R., Kim-Schmid, J. "Where AI Can — and Can’t — Help Talent Management." Harvard Business Review. 2022.
Albert, Michael. "Learning in Online Principal-Agent Interactions: The Power of Menus." Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-24), Vancouver, Canada, 2024.
Cian, Luca. "News from Generative Artificial Intelligence is Believed." (pp. 97-106). Association for Computing Machinery. 2022.
Freeman, Edward. "Efficient Resource Allocation With Secretive Agents." (pp. 272-278). International Joint Conferences on Artificial Intelligence Organization. 2022.
V. Kumar, Bharath Rajan, Rajkumar Venkatesan, Jim Lecinski (2019), “Understanding the Role of Artificial Intelligence in Personalized Engagement Marketing” California Management Review.
Reinartz, Werner, and Venkatesan, Rajkumar. (2022), “A Better Way to Calculate Marketing ROI,” Harvard Business Review, 108-110.
Korinek, A., & Suh, D. (2024). Scenarios for the Transition to AGI (No. w32255). National Bureau of Economic Research. https://doi.org/10.3386/w32255
Our research focuses on “the intersection of data science and business” in light of the rapid innovation in data and technology taking place in the business world.
However, the white space that exists at this intersection is unmanageably large and offers little guidance in how and where to deploy the limited resources available to us. As a result, and guided by the mission described above, we defined six areas of focus within this white space that align with priorities previously identified by Darden, the School of Data Science, and the University of Virginia. We call them “themes,” and they are as follows:
- Bias and Misinformation. Exploring algorithms and data-intensive business practices that increase equity and promote truthfulness in business and in society.
- Analytical Leadership. Managing and leading analytical individuals, high-performing teams, and distinctive organizations in the face of an explosion of data and the near ubiquity of technologies that enable leaders to use or misuse it.
- Healthy Choices. Understanding and influencing consumer and health care professional decision-making behavior through interventions, experiments, and analysis using data and technology, with the objective of improving health and better managing care.
- Low/No-code. Expanding the accessibility of data science tools and methods beyond the most sophisticated practitioners to enable application of data science to difficult challenges in business by a broader range of individuals, teams, and organizations.
- Entrepreneurship. Developing the data science skills of entrepreneurs and advancing the translation of innovations in data science into commercial businesses, in Charlottesville and across the region.
- Social Impact. Enhancing the availability of data and data science to leaders in organizations, such as local and regional governments and nonprofits, that are focused on issues and opportunities to positively impact communities and society.
Academic Research
Longoni, C., Cian, L., Kyung, E. J. "Algorithmic Transference: People Overgeneralize Failures of AI in the Government." Journal of Marketing Research, 60(1), 170–188. 2023.
Guo, X., Grushka-Cockayne, Y., De Reyck, B. "Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning." Manufacturing & Service Operations Management, 24(6), 2797-3306. 2022.
Raveendhran, R., Kim-Schmid, J. "Where AI Can — and Can’t — Help Talent Management." Harvard Business Review. 2022.
Other Publications
Albert, Michael. "Learning in Online Principal-Agent Interactions: The Power of Menus." Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-24), Vancouver, Canada, 2024.
Reinartz, Werner, and Venkatesan, Rajkumar. (2022), “A Better Way to Calculate Marketing ROI,” Harvard Business Review, 108-110.
Korinek, A., & Suh, D. (2024). Scenarios for the Transition to AGI (No. w32255). National Bureau of Economic Research. https://doi.org/10.3386/w32255
Longoni, C., Cian, L., Kyung, E. J. "Algorithmic Transference: People Overgeneralize Failures of AI in the Government." Journal of Marketing Research, 60(1), 170–188. 2023.
Tomislav, P., Cian, L., Franc, R., et al. "Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning." PNAS Nexus, 1(3), 1-15. 2022.
Longoni, C., Cian, L. "Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The 'Word-of-Machine' Effect." Journal of Marketing, 86(1), 91-108. 2022.
Guo, X., Grushka-Cockayne, Y., De Reyck, B. "Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning." Manufacturing & Service Operations Management, 24(6), 2797-3306. 2022.
Raveendhran, R., Kim-Schmid, J. "Where AI Can — and Can’t — Help Talent Management." Harvard Business Review. 2022.
Albert, Michael. "Learning in Online Principal-Agent Interactions: The Power of Menus." Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-24), Vancouver, Canada, 2024.
Cian, Luca. "News from Generative Artificial Intelligence is Believed." (pp. 97-106). Association for Computing Machinery. 2022.
Freeman, Edward. "Efficient Resource Allocation With Secretive Agents." (pp. 272-278). International Joint Conferences on Artificial Intelligence Organization. 2022.
V. Kumar, Bharath Rajan, Rajkumar Venkatesan, Jim Lecinski (2019), “Understanding the Role of Artificial Intelligence in Personalized Engagement Marketing” California Management Review.
Reinartz, Werner, and Venkatesan, Rajkumar. (2022), “A Better Way to Calculate Marketing ROI,” Harvard Business Review, 108-110.
Korinek, A., & Suh, D. (2024). Scenarios for the Transition to AGI (No. w32255). National Bureau of Economic Research. https://doi.org/10.3386/w32255