Machinery and Machining - Publications - Faculty & Research - Harvard Business School

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February 2019
Case

Miroglio Fashion (A)

By: Sunil Gupta and David Lane

Francesco Cavarero, chief information officer of Miroglio Fashion, Italy’s third-largest retailer of women’s apparel, was trying to bring analytical rigor to the company’s forecasting and inventory management decisions. But fashion is inherently hard to predict. Can... View Details

Keywords: Inventory Management; Demand Forecasting; Artificial Intelligence; Machine Learning; Forecasting and Prediction; Operations; Management; Decision Making; AI and Machine Learning; Apparel and Accessories Industry; Apparel and Accessories Industry

October 2018
Case

American Family Insurance and the Artificial Intelligence Opportunity

By: Rajiv Lal and Scott Johnson

April 2017
Case

The Future of Patent Examination at the USPTO

By: Prithwiraj Choudhury, Tarun Khanna and Sarah Mehta

The U.S. Patent and Trademark Office (USPTO) is the federal government agency responsible for evaluating and granting patents and trademarks. In 2015, the USPTO employed approximately 8,000 patent examiners who granted nearly 300,000 patents to inventors. As of April... View Details

Keywords: Machine Learning; Telework; Collaborating With Unions; Human Resources; Recruitment; Retention; Intellectual Property; Copyright; Patents; Trademarks; Knowledge Sharing; Technology Adoption; Organizational Change and Adaptation; Performance Productivity; Performance Improvement; District of Columbia

2021
Working Paper

The Origins of CE Marking: Standards, Business, and the European Market in the 1980s–1990s

By: Grace Ballor

Many products—from consumer electronics to machinery to children’s toys—bear the CE Mark, the symbol of conformity to the ‘essential requirements’ of European standards governed by the process of CE Marking. This working paper traces the development of the system of... View Details

Keywords: Business And Government; Market Liberalization; Standards; Markets; Trade; Integration; Business History; Globalization; Business and Government Relations; Europe; European Union

October 2015 (Revised October 2016)
Case

Building Watson: Not So Elementary, My Dear! (Abridged)

By: Willy C. Shih

This case is set inside IBM Research's efforts to build a computer that can successfully take on human challengers playing the game show Jeopardy! It opens with the machine named Watson offering the incorrect answer "Toronto" to a seemingly simple question during the... View Details

Keywords: Analytics; Big Data; Business Analytics; Product Development Strategy; Machine Learning; Machine Intelligence; Artificial Intelligence; Product Development; AI and Machine Learning; Information Technology; Analytics and Data Science; Information Technology Industry; United States

2020
Article

Humanizing Management and Innovation

By: Hirotaka Takeuchi

This article is an excerpt from The Wise Company book that Ikujiro Nonaka and I published in October 2019 from Oxford University Press. It is a sequel to The Knowledge-Creating Company book we published 25 years ago. As our thinking evolved from information to... View Details

Keywords: Knowledge Creation; Knowledge Practice; Phronesis; Practical Wisdom; Ba; Continuous Innovation; Fusion Of Analog And Digital; Management As A Way Of Life; Management Style; Emotions; Innovation and Management

April 2014 (Revised March 2015)
Case

GE and the Industrial Internet

By: Karim R. Lakhani, Marco Iansiti and Kerry Herman

CEO Jeff Immelt considers whether GE is moving fast enough on its new Industrial Internet initiative. The undertaking includes building out an Industrial Internet, connecting machines and devices, collecting their data and operations, and providing services to clients... View Details

Research Summary

Overview

By: Himabindu Lakkaraju

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

Keywords: Artificial Intelligence; Machine Learning; Decision Analysis; Decision Support

Mar 2020
Conference Presentation

A New Analysis of Differential Privacy's Generalization Guarantees

By: Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Moshe Shenfeld

We give a new proof of the "transfer theorem" underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially private and sample-accurate is also accurate out-of-sample. Our new proof is elementary and... View Details

Keywords: Machine Learning; Transfer Theorem; Mathematical Methods

April 2024
Article

Detecting Routines: Applications to Ridesharing CRM

By: Ryan Dew, Eva Ascarza, Oded Netzer and Nachum Sicherman

Routines shape many aspects of day-to-day consumption. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines—which we define as repeated behaviors with recurring, temporal... View Details

Keywords: Ride-sharing; Routine; Machine Learning; Customer Relationship Management; Consumer Behavior; Segmentation

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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

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Forthcoming
Article

Serving with a Smile on Airbnb: Analyzing the Economic Returns and Behavioral Underpinnings of the Host’s Smile

By: Shunyuan Zhang, Elizabeth Friedman, Kannan Srinivasan, Ravi Dhar and Xupin Zhang

Non-informational cues, such as facial expressions, can significantly influence judgments and interpersonal impressions. While past research has explored how smiling affects business outcomes in offline or in-store contexts, relatively less is known about how smiling... View Details

Keywords: Sharing Economy; Airbnb; Image Feature Extraction; Machine Learning; Facial Expressions; Prejudice and Bias; Nonverbal Communication; E-commerce; Consumer Behavior; Perception

2021
Conference Presentation

An Algorithmic Framework for Fairness Elicitation

By: Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Leon Roth, Logan Stapleton and Zhiwei Steven Wu

We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more complex and nuanced and thus require elicitation from individual or collective stakeholders.... View Details

Keywords: Algorithmic Fairness; Machine Learning; Fairness; Framework; Mathematical Methods

December 1, 2021
Article

Do You Know How Your Teams Get Work Done?

By: Rohan Narayana Murty, Rajath B. Das, Scott Duke Kominers, Arjun Narayan, Suraj Srinivasan, Tarun Khanna and Kartik Hosanagar

In a research study at four Fortune 500 companies, when managers were asked about their teams’ work, on average they either did not know or could not remember 60% of the work their teams do. This is a major problem because it can lead to unrealistic digital... View Details

Keywords: Leading Teams; Work Recall Gap; Machine Learning; Algorithms; Groups and Teams; Management; Technological Innovation

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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

2020
Article

Public Sentiment and the Price of Corporate Sustainability

By: George Serafeim

Combining corporate sustainability performance scores based on environmental, social, and governance (ESG) data with big data measuring public sentiment about a company’s sustainability performance, I find that the valuation premium paid for companies with strong... View Details

Keywords: Sustainability; ESG; ESG (Environmental, Social, Governance) Performance; Investment Management; Investment Strategy; Big Data; Machine Learning; Environment; Environmental Sustainability; Corporate Governance; Performance; Asset Pricing; Investment; Management; Strategy; Human Capital; Public Opinion; Value; Analytics and Data Science

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January 2024 (Revised January 2025)
Case

Huawei: Resilience amid Autarky and Adversity

By: William C. Kirby and Daniel Fu

In September 2023, Huawei made a dramatic return to the global smartphone space with the launch of its Mate 60 Pro smartphone, equipped with an indigenously designed, 7nm chip. This came despite a myriad of export controls and restrictions imposed against the company... View Details

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

Teaching Interest

Empirical Technology and Operations Management Course

By: Himabindu Lakkaraju

I taught a set of lectures on "Introduction to Machine Learning for Social Scientists" as part of this required course for first year PhD students. This module familiarizes students with all the basic concepts in machine learning, their implementations, as well as the... View Details

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

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