Publications

17 May 2022
News

Harvard Professor Reveals Why You Feel Lost & Unhappy in Life | Arthur Brooks on Impact Theory

October 2023
Article

Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA

By: Matthew S. Johnson, David I. Levine and Michael W. Toffel

We study how a regulator can best target inspections. Our case study is a U.S. Occupational Safety and Health Administration (OSHA) program that randomly allocated some inspections. On average, each inspection averted 2.4 serious injuries (9%) over the next five years.... View Details

Keywords: Safety Regulations; Regulations; Regulatory Enforcement; Machine Learning Models; Safety; Operations; Service Operations; Production; Forecasting and Prediction; Decisions; United States

May 2021
Case

The International Space Station, Principal-Agent Problems, and NASA's Quest to Keep Humans in Space

By: Matthew Weinzierl and Mehak Sarang

In building the International Space Station (ISS), NASA opened the door to the development of a robust in-space economy in low-Earth Orbit, and yet the decision to build the station, and continue to extend its lifetime, placed a huge burden on NASA’s Human Spaceflight... View Details

Keywords: Aerospace; Nasa; Space Economy; Principal-agent Theory; Policy; Commercialization; Aerospace Industry

Educators

2014
Article

Corporate Social Responsibility Reporting in China: Symbol or Substance?

By: Christopher Marquis and Cuili Qian

This study focuses on how and why firms strategically respond to government signals regarding appropriate corporate activity. We integrate institutional theory and research on corporate political strategy to develop a political dependence model that explains (a) how... View Details

Keywords: Institutional Theory; Political Strategy; Non-market Strategy; China; Corporate Social Responsibility; Corporate Disclosure; Corporate Social Responsibility and Impact; Emerging Markets; Government and Politics; China

2018
Working Paper

Two Hundred Years of Health and Medical Care: The Importance of Medical Care for Life Expectancy Gains

By: Maryaline Catillon, David Cutler and Thomas Getzen

Using two hundred years of national and Massachusetts data on medical care and health, we examine how central medical care is to life expectancy gains. While common theories about medical care cost growth stress growing demand, our analysis highlights the importance of... View Details

Keywords: Mortality; Life Expectancy; Medical Care; Productivity; Public Health; Healthcare Spending; Spending Per Year Of Life Gained; Personal Medicine; Technophysio Evolution; Health; Economics; Health Care and Treatment; Spending; Data and Data Sets; Health Industry

14 Aug 2017
Conference Presentation

A Convex Framework for Fair Regression

By: Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel and Aaron Roth

We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems. These regularizers all enjoy convexity, permitting fast optimization, and they span the range from notions of group fairness to strong individual fairness. By varying... View Details

Keywords: Regression Models; Machine Learning; Fairness; Framework; Mathematical Methods

Article

Towards Robust and Reliable Algorithmic Recourse

By: Sohini Upadhyay, Shalmali Joshi and Himabindu Lakkaraju

As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan approvals), there has been growing interest in post-hoc techniques which provide recourse to affected individuals. These techniques generate recourses under the assumption... View Details

Keywords: Machine Learning Models; Algorithmic Recourse; Decision Making; Forecasting and Prediction

November 2013
Article

Learning from My Successes and from Others' Failures: Evidence from Minimally Invasive Cardiac Surgery

By: D. KC, B. Staats and F. Gino

Learning from past experience is central to an organization's adaptation and survival. A key dimension of prior experience is whether an outcome was successful or unsuccessful. While empirical studies have investigated the effects of success and failure in... View Details

Keywords: Healthcare; Health Care; Knowledge Work; Attribution Theory; Quality; Success; Medical Specialties; Health Care and Treatment; Failure; Learning; Health Industry

October 2023 (Revised June 2024)
Case

ReUp Education: Can AI Help Learners Return to College?

By: Kris Ferreira, Christopher Thomas Ryan and Sarah Mehta

Founded in 2015, ReUp Education helps “stopped out students”—learners who have stopped making progress towards graduation—achieve their college completion goals. The company relies on a team of success coaches to engage with learners and help them reenroll. In 2019,... View Details

Keywords: AI; Algorithms; Machine Learning; Edtech; Education Technology; Analysis; Higher Education; AI and Machine Learning; Customization and Personalization; Failure; Education Industry; Technology Industry; United States

Educators

November 2022
Article

A Language-Based Method for Assessing Symbolic Boundary Maintenance between Social Groups

By: Anjali M. Bhatt, Amir Goldberg and Sameer B. Srivastava

When the social boundaries between groups are breached, the tendency for people to erect and maintain symbolic boundaries intensifies. Drawing on extant perspectives on boundary maintenance, we distinguish between two strategies that people pursue in maintaining... View Details

Keywords: Culture; Machine Learning; Natural Language Processing; Symbolic Boundaries; Organizations; Boundaries; Social Psychology; Interpersonal Communication; Organizational Culture

Article

Oracle Efficient Private Non-Convex Optimization

By: Seth Neel, Aaron Leon Roth, Giuseppe Vietri and Zhiwei Steven Wu

One of the most effective algorithms for differentially private learning and optimization is objective perturbation. This technique augments a given optimization problem (e.g. deriving from an ERM problem) with a random linear term, and then exactly solves it.... View Details

Keywords: Machine Learning; Algorithms; Objective Perturbation; Mathematical Methods

Article

Counterfactual Explanations Can Be Manipulated

By: Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju and Sameer Singh

Counterfactual explanations are useful for both generating recourse and auditing fairness between groups. We seek to understand whether adversaries can manipulate counterfactual explanations in an algorithmic recourse setting: if counterfactual explanations indicate... View Details

Keywords: Machine Learning Models; Counterfactual Explanations

February 2015 (Revised September 2016)
Teaching Note

Making stickK Stick: The Business of Behavioral Economics

By: Leslie K. John and Michael Norton

Email mking@hbs.edu for a courtesy copy.

This Teaching Note explains the theory of the case and teaching plan for the case: Making sticK Stick: The Business of Behavioral Economics (514019). The case focuses on a... View Details

Keywords: Behavioral Economics; Behavior Change; B2B Vs. B2C; Human Resource Management; Marketing Of Innovations; Health ＆ Wellness; Weight Loss; Charitable Giving; Marketing; Consumer Behavior; Entrepreneurship; Internet and the Web; Health; Business Model; Sales; Human Resources; Health Industry; United States

October–December 2022
Article

Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius

Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed... View Details

Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; AI and Machine Learning; Forecasting and Prediction

Register to Read

Article

Deep Down My Enemy Is Good: Thinking about the True Self Reduces Intergroup Bias

By: Julian De Freitas and Mina Cikara

Intergroup bias—preference for one's in-group relative to out-groups—is one of the most robust phenomena in all of psychology. Here we investigate whether a positive bias that operates at the individual-level, belief in a good true self, may be leveraged to reduce... View Details

Keywords: Intergroup Bias; True Self; Essentialism; Lay Theories

July 2019 (Revised November 2019)
Case

Osaro: Picking the Best Path

By: William R. Kerr, James Palano and Bastiane Huang

The founder of Osaro saw the potential of deep reinforcement learning to allow robots to be applied to new applications. Osaro targeted warehousing, already a dynamic industry for robotics and automation, for its initial product—a system which would allow robotic arms... View Details

Educators

December 2020
Supplement

VIA Science (B)

By: Juan Alcácer, Rembrand Koning, Annelena Lobb and Kerry Herman

Via (a) captures the early days of the data analytics startup as founders Gounden and Ravanis considered which markets offer the right opportunities for their firm and what kinds of experiments will help them narrow their choice. Supplement Via (b) reveals the... View Details

Keywords: Data Analytics; Machine Learning; Artificial Intelligence; Strategy; Business Startups; AI and Machine Learning; Telecommunications Industry; Utilities Industry; United States; Japan