Anh Luong
University of Warwick, Warwick Business School, Faculty Member
- Anh Luong is an Assistant Professor in the Operations group at the Warwick Business School, University of Warwick. Anh researches topics at the intersection of Artificial Intelligence, decision-making, and collaboration in organizations. She has examined bias/fairness in algorithmic decision-maki... moreAnh Luong is an Assistant Professor in the Operations group at the Warwick Business School, University of Warwick. Anh researches topics at the intersection of Artificial Intelligence, decision-making, and collaboration in organizations. She has examined bias/fairness in algorithmic decision-making, human-machine collaboration, collaborative decision-making, data visualization, and business analytics.edit
Slides for our talk given at Workshop on Information Systems and Economics (WISE) 2021, December 16, Austin, TX
Research Interests:
Slides for our talk given at Workshop on Information Systems and Economics (WISE) 2019, Munich, Germany
Research Interests:
** Proceedings of our talk given at the Workshop on e-Business 2018, Santa Clara, CA ** "Big Data" marks one of the biggest opportunities and also challenges of modern day organizations. Not all data is equal-only some contain... more
** Proceedings of our talk given at the Workshop on e-Business 2018, Santa Clara, CA **
"Big Data" marks one of the biggest opportunities and also challenges of modern day organizations. Not all data is equal-only some contain insights. To make sound decisions, firms need not only process data, but also extract meaningful information, and present it in such a way that can illuminate perceptions. Intrigued by this topic's current challenges, this study examines how varied methods of providing information affect collaborative decision-making performance. To examine the research question, an electronic platform in the laboratory is designed to implement a collaborative decision-making experiment using game theory. Experimental economics is used as the main research methodology, featuring financial incentives in the experiments in order to induce subjects' rationally strategic behaviors. The research wishes to yield meaningful insights that bear implications for many contexts, most important of which are inter-and intra-organizational decision making, and group collaboration.
"Big Data" marks one of the biggest opportunities and also challenges of modern day organizations. Not all data is equal-only some contain insights. To make sound decisions, firms need not only process data, but also extract meaningful information, and present it in such a way that can illuminate perceptions. Intrigued by this topic's current challenges, this study examines how varied methods of providing information affect collaborative decision-making performance. To examine the research question, an electronic platform in the laboratory is designed to implement a collaborative decision-making experiment using game theory. Experimental economics is used as the main research methodology, featuring financial incentives in the experiments in order to induce subjects' rationally strategic behaviors. The research wishes to yield meaningful insights that bear implications for many contexts, most important of which are inter-and intra-organizational decision making, and group collaboration.
Research Interests:
Human experts are being increasingly required to work with artificial intelligence and machine learning (AI/ML) in organizational decision-making. Using a large-scale historic dataset, we design and run an economic experiment where... more
Human experts are being increasingly required to work with artificial intelligence and machine learning (AI/ML) in organizational decision-making. Using a large-scale historic dataset, we design and run an economic experiment where financially incentivized participants evaluate loan applications with the aid of an AI/ML. We find that firm performance when employing AI/ML for complex decision-making depends on both reliable technology and well-aligned organizational practices. Human and machine working together can beat the machine operating alone, if there is an incentive alignment mechanism. Most importantly, when both high accuracy AI/ML and well-aligned incentive structures are put into place, firms maximize their profits. We also find that, only when the AI/ML in use has adequate accuracy can the human-machine team excel humans operating on their own. Otherwise, with a sub-par accuracy AI/ML, even when it is significantly better than chance, humans are better off working alone. We contribute to the emerging algorithmic decision-making literature by introducing the incentive alignment perspectives. To the best of our knowledge, we are the first in this area to examine the impacts of both AI/ML systems' properties and organizational practices, in addition to accounting for the characteristics of human decision makers. Importantly, we examine and highlight the importance of their interdependent effect on maximizing organizational outcomes. We also contribute to the literature investigating which tasks should and should not be automated. Our comparison between man-machine collaboration and machine alone is especially important given rising concerns about automation replacing humans and exacerbating inequality. Our findings hold implications for firms wishing to build sustainable human-machine collaboration, that not only serves to increase organizational financial gains, but more importantly, also to solidify the role of humans in the current employment landscape that is constantly changing due to the rapid advances in AI/ML every day.