Publications

2022
Working Paper

Machine Learning Models for Prediction of Scope 3 Carbon Emissions

By: George Serafeim and Gladys Vélez Caicedo

For most organizations, the vast amount of carbon emissions occur in their supply chain and in the post-sale processing, usage, and end of life treatment of a product, collectively labelled scope 3 emissions. In this paper, we train machine learning algorithms on 15... View Details

Keywords: Carbon Emissions; Climate Change; Environment; Carbon Accounting; Machine Learning; Artificial Intelligence; Digital; Data Science; Environmental Sustainability; Environmental Management; Environmental Accounting

SSRN

November 2024 (Revised January 2025)
Case

MiDAS: Automating Unemployment Benefits

By: Shikhar Ghosh and Shweta Bagai

In 2015, the state of Michigan considered whether to nominate its Michigan Integrated Data Automated System (MiDAS) for a prestigious state technology award. Launched in 2013 amid severe budget pressures, the $47 million automated fraud detection system was designed to... View Details

Educators

Aug 2014
Working Paper

Defining Clusters of Related Industries

This paper develops a novel clustering algorithm that systematically generates and assesses sets of cluster definitions (i.e., groups of closely related industries). View Details

September 2022 (Revised November 2022)
Teaching Note

PittaRosso: Artificial Intelligence-Driven Pricing and Promotion

By: Ayelet Israeli

Teaching Note for HBS Case No. 522-046. View Details

Article

Testing Substitutability

By: John William Hatfield, Nicole Immorlica and Scott Duke Kominers

We provide an algorithm for testing the substitutability of a length-N preference relation over a set of contracts X in time O(|X|3⋅N3). Access to the preference relation is essential for this result: We show that a substitutability-testing algorithm with access only... View Details

Keywords: Substitutability; Matching; Communication Complexity; Preference Elicitation; Marketplace Matching; Communication; Mathematical Methods; Economics

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Research Summary

Understanding the Limitations of Model Explanations

By: Himabindu Lakkaraju

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

Research Summary

Overview

By: Kris Johnson Ferreira

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

Keywords: E-commerce; Analytics; Revenue Management; Pricing; Assortment Planning; Field Experiments; Operations; Supply Chain; Supply Chain Management; Retail Industry

March 2022
Article

Learning to Rank an Assortment of Products

By: Kris Ferreira, Sunanda Parthasarathy and Shreyas Sekar

We consider the product ranking challenge that online retailers face when their customers typically behave as “window shoppers”: they form an impression of the assortment after browsing products ranked in the initial positions and then decide whether to continue... View Details

Keywords: Online Learning; Product Ranking; Assortment Optimization; Learning; Internet and the Web; Product Marketing; Consumer Behavior; E-commerce

08 May 2018
First Look

First Look at New Research and Ideas, May 8, 2018

https://www.hbs.edu/faculty/Pages/item.aspx?num=52570 Algorithm Appreciation: People Prefer Algorithmic to Human Judgment By: Logg, Jennifer M., Julia A. Minson, and Don A. Moore Abstract—Even though... View Details

Keywords: Sean Silverthorne

November–December 2018
Article

Online Network Revenue Management Using Thompson Sampling

By: Kris J. Ferreira, David Simchi-Levi and He Wang

We consider a network revenue management problem where an online retailer aims to maximize revenue from multiple products with limited inventory constraints. As common in practice, the retailer does not know the consumer's purchase probability at each price and must... View Details

Keywords: Online Marketing; Revenue Management; Revenue; Management; Marketing; Internet and the Web; Price; Mathematical Methods

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17 Sep 2021
News

AI Can Help Address Inequity — If Companies Earn Users’ Trust

27 May 2015
Blog Post

What is an HBS Section?

10 sections of 90+ people in your first year. You find out during your first week at HBS which section you’ll be in (A-J), and they use an algorithm to make sure each section has a diverse cross-section of people from different countries,... View Details

November–December 2024
Article

Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing

By: Kirk Bansak and Elisabeth Paulson

This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a multi-year pilot in Switzerland, seeks to maximize the average predicted employment... View Details

Keywords: AI and Machine Learning; Refugees; Geographic Location; Employment

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17 May 2022
Cold Call Podcast

Delivering a Personalized Shopping Experience with AI

Keywords: Re: Jill J. Avery

Teaching Interest

Big Data Analytics and Machine Learning

Big data in the context of marketing, management, and innovation strategy. Machine Learning algorithms and tools.
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Keywords: Big Data; Machine Learning; Analytics

Working Paper

Group Fairness in Dynamic Refugee Assignment

By: Daniel Freund, Thodoris Lykouris, Elisabeth Paulson, Bradley Sturt and Wentao Weng

Ensuring that refugees and asylum seekers thrive (e.g., find employment) in their host countries is a profound humanitarian goal, and a primary driver of employment is the geographic location within a host country to which the refugee or asylum seeker is... View Details

Keywords: Refugees; Geographic Location; Mathematical Methods; Employment; Fairness

September 2020 (Revised July 2022)
Supplement

Spreadsheet Supplement to Artea (B) and (C)

By: Eva Ascarza and Ayelet Israeli

Spreadsheet Supplement to "Artea (B): Including Customer-level Demographic Data" and "Artea (C): Potential Discrimination through Algorithmic Targeting" View Details

Keywords: Gender; Race; Diversity; Marketing; Customer Relationship Management; Demographics; Prejudice and Bias; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States