Publications
Publications
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Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation
By: Maya Balakrishnan, Kris Ferreira and Jordan Tong
Even if algorithms make better predictions than humans on average, humans may sometimes have private information which an algorithm does not have access to that can improve performance. How can we help humans effectively use and adjust recommendations made by... View Details
- Article
Ideation with Generative AI—In Consumer Research and Beyond
By: Julian De Freitas, G. Nave and Stefano Puntoni
The use of large language models (LLMs) in consumer research is rapidly evolving, with applications including synthetic data generation, data analysis, and more. However, their role in creative ideation—a cornerstone of consumer research—remains underexplored. Drawing... View Details
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Interpretability and Explainability in Machine Learning
As machine learning models are increasingly being employed to aid decision makers in high-stakes settings such as healthcare and criminal justice, it is important to ensure that the decision makers correctly understand and consequent trust the functionality of these... View Details
- Research Summary
Making Machine Learning Models Fair
The goal of this research direction is to ensure that the machine learning models we build and deploy do not discriminate against individuals from minority groups. View Details
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Making Machine Learning Models Interpretable
I work on developing various tools and methodologies which can help decision makers (e.g., doctors, managers) to better understand the predictions of machine learning models. View Details
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Making Machine Learning Robust to Adversarial Attacks
The goal of this research is to ensure that machine learning models that we build and deploy are not easily susceptible to attacks by adversarial or malicious entities. View Details
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Overview
Public entrepreneurship, entrepreneurship, leadership, business and government, cities, artificial intelligence View Details
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By: V.G. Narayanan
I teach accounting to MBA students, executives, and Harvard Extension School students. I teach topics from both financial and managerial accounting. I also train professors in teaching by the case method. View Details
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Prithwiraj (Raj) Choudhury is the Lumry Family Associate Professor at the Harvard Business School. He was an Assistant Professor at Wharton prior to joining Harvard. His research is focused on studying the Future of Work, especially the changing Geography of Work. In... View Details
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By: Isamar Troncoso
Professor Troncoso's research explores problems related to digital marketplaces and AI applications in marketing, and combines toolkits from econometrics, causal inference, and machine learning. She has studied how different platform design choices can lead to... View Details
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I develop machine learning tools and techniques which enable human decision makers to make better decisions. More specifically, my research addresses the following fundamental questions pertaining to human and algorithmic decision-making:
1. How to build... View Details
1. How to build... View Details
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By: Shunyuan Zhang
Professor Zhang uses machine learning to address marketing problems that have arisen within the nascent sharing economy. She conducts rigorous analyses of structured and unstructured data generated by new sharing economy platforms to address important issues emerging... View Details
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By: Srikant M. Datar
Professor Datar has several research and course development interests. His initial areas of research interest were in cost management and management control, strategy implementation and governance. Over the last few years his areas of interest are management education,... View Details
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Engaged with field work in East Africa, South Asia, and in several large hybrid organizations in the United States, Professor Whillans places a focus on exploring questions with strong theoretical motivation in the social psychological literature and relevant... View Details
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Professor Ferreira's research primarily focuses on how retailers can use algorithms to make better revenue management decisions, including pricing, product display, and assortment planning. In the retail industry, anticipating consumer demand is arguably one of the... View Details
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Understanding the Limitations of Model Explanations
The goal of this research is to understand how adversaries can exploit various algorithms used for explaining complex machine learning models with an intention to mislead end users. For instance, can adversaries trick these algorithms into masking their racial and... View Details
- Article
Unregulated Emotional Risks of AI Wellness Apps
By: Julian De Freitas and Glenn Cohen
We propose that AI-driven wellness apps powered by large language models can foster extreme emotional attachments and dependencies akin to human relationships—posing risks like ambiguous loss and dysfunctional dependence—that challenge current regulatory frameworks and... View Details
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