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- October 2018
- Article
The Operational Value of Social Media Information
By: Ruomeng Cui, Santiago Gallino, Antonio Moreno and Dennis J. Zhang
While the value of using social media information has been established in multiple business contexts, the field of operations and supply chain management have not yet explored the possibilities it offers in improving firms' operational decisions. This study attempts to...
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Cui, Ruomeng, Santiago Gallino, Antonio Moreno, and Dennis J. Zhang. "The Operational Value of Social Media Information." Special Issue on Big Data in Supply Chain Management. Production and Operations Management 27, no. 10 (October 2018): 1749–1774.
- 2020
- Working Paper
Machine Learning for Pattern Discovery in Management Research
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used as an observation for further inductive or abductive research, but should not be treated as the result of a...
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Keywords:
Machine Learning;
Theory Building;
Induction;
Decision Trees;
Random Forests;
K-nearest Neighbors;
Neural Network;
P-hacking;
Analytics and Data Science;
Analysis
Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning for Pattern Discovery in Management Research." Harvard Business School Working Paper, No. 19-032, September 2018. (Revised June 2020.)
- 2019
- Working Paper
Machine Learning Approaches to Facial and Text Analysis: Discovering CEO Oral Communication Styles
By: Prithwiraj Choudhury, Dan Wang, Natalie A. Carlson and Tarun Khanna
We demonstrate how a novel synthesis of three methods—(1) unsupervised topic modeling of text data to generate new measures of textual variance, (2) sentiment analysis of text data, and (3) supervised ML coding of facial images with a cutting-edge convolutional neural...
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Choudhury, Prithwiraj, Dan Wang, Natalie A. Carlson, and Tarun Khanna. "Machine Learning Approaches to Facial and Text Analysis: Discovering CEO Oral Communication Styles." Harvard Business School Working Paper, No. 18-064, January 2018. (Revised May 2019.)
- Article
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
The most prevalent notions of fairness in machine learning are statistical definitions: they fix a small collection of pre-defined groups, and then ask for parity of some statistic of the classifier (like classification rate or false positive rate) across these groups....
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Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness." Proceedings of the International Conference on Machine Learning (ICML) 35th (2018).
- October 2017 (Revised April 2018)
- Case
Improving Worker Safety in the Era of Machine Learning (A)
By: Michael W. Toffel, Dan Levy, Jose Ramon Morales Arilla and Matthew S. Johnson
Managers make predictions all the time: How fast will my markets grow? How much inventory do I need? How intensively should I monitor my suppliers? Which potential customers will be most responsive to a particular marketing campaign? Which job candidates should I...
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Keywords:
Machine Learning;
Policy Implementation;
Empirical Research;
Inspection;
Occupational Safety;
Occupational Health;
Regulation;
Analysis;
Forecasting and Prediction;
Policy;
Operations;
Supply Chain Management;
Safety;
Manufacturing Industry;
Construction Industry;
United States
Toffel, Michael W., Dan Levy, Jose Ramon Morales Arilla, and Matthew S. Johnson. "Improving Worker Safety in the Era of Machine Learning (A)." Harvard Business School Case 618-019, October 2017. (Revised April 2018.)
- 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...
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Berk, Richard, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. "A Convex Framework for Fair Regression." Paper presented at the 4th Workshop on Fairness, Accountability, and Transparency in Machine Learning, Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), August 14, 2017.
- January 2017 (Revised March 2017)
- Case
IBM Transforming, 2012–2016: Ginni Rometty Steers Watson
By: Rosabeth Moss Kanter and Jonathan Cohen
To transform IBM for the next technology wave, Ginni Rometty, who became CEO in 2012, led divestment of declining businesses, made acquisitions in digital innovation and cloud computing, formed partnerships with former competitors such as Apple and tech startups, and...
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Keywords:
Digital;
Technological Change;
Artificial Intelligence;
Data;
IBM;
Watson;
Internet Of Things;
Innovation and Invention;
Management;
Sales;
Information Technology;
Technological Innovation;
Transformation;
AI and Machine Learning
Kanter, Rosabeth Moss, and Jonathan Cohen. "IBM Transforming, 2012–2016: Ginni Rometty Steers Watson." Harvard Business School Case 317-046, January 2017. (Revised March 2017.)
- 18 Nov 2016
- Conference Presentation
Rawlsian Fairness for Machine Learning
By: Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel and Aaron Leon Roth
Motivated by concerns that automated decision-making procedures can unintentionally lead to discriminatory behavior, we study a technical definition of fairness modeled after John Rawls' notion of "fair equality of opportunity". In the context of a simple model of...
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Joseph, Matthew, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Leon Roth. "Rawlsian Fairness for Machine Learning." Paper presented at the 3rd Workshop on Fairness, Accountability, and Transparency in Machine Learning, Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), November 18, 2016.
- Article
Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy
By: Edward Glaeser, Andrew Hillis, Scott Duke Kominers and Michael Luca
The proliferation of big data makes it possible to better target city services like hygiene inspections, but city governments rarely have the in-house talent needed for developing prediction algorithms. Cities could hire consultants, but a cheaper alternative is to...
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Keywords:
User-generated Content;
Operations;
Tournaments;
Policy-making;
Machine Learning;
Online Platforms;
Analytics and Data Science;
Mathematical Methods;
City;
Infrastructure;
Business Processes;
Government and Politics
Glaeser, Edward, Andrew Hillis, Scott Duke Kominers, and Michael Luca. "Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy." American Economic Review: Papers and Proceedings 106, no. 5 (May 2016): 114–118.
- Article
Productivity and Selection of Human Capital with Machine Learning
By: Aaron Chalfin, Oren Danieli, Andrew Hillis, Zubin Jelveh, Michael Luca, Jens Ludwig and Sendhil Mullainathan
Keywords:
Analytics and Data Science;
Selection and Staffing;
Performance Productivity;
Mathematical Methods;
Policy
Chalfin, Aaron, Oren Danieli, Andrew Hillis, Zubin Jelveh, Michael Luca, Jens Ludwig, and Sendhil Mullainathan. "Productivity and Selection of Human Capital with Machine Learning." American Economic Review: Papers and Proceedings 106, no. 5 (May 2016): 124–127.
- 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...
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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
Shih, Willy C. "Building Watson: Not So Elementary, My Dear! (Abridged)." Harvard Business School Case 616-025, October 2015. (Revised October 2016.)
- September 2011 (Revised July 2012)
- Case
Building Watson: Not So Elementary, My Dear!
By: Willy 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...
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Keywords:
Technological Innovation;
Standards;
Product Development;
Organizational Change and Adaptation;
Mathematical Methods;
Research and Development;
Information Technology
Shih, Willy. "Building Watson: Not So Elementary, My Dear!" Harvard Business School Case 612-017, September 2011. (Revised July 2012.)
- September 2009
- Article
A Detailed Analysis of the Reduction Mammaplasty Learning Curve: A Statistical Process Model for Approaching Surgical Performance Improvement
By: Matthew Carty MD, Rodney Chan, Robert S. Huckman, Daniel C. Snow and Dennis Orgill
Background: The increased focus on quality and efficiency improvement within academic surgery has met with variable success among plastic surgeons. Traditional surgical performance metrics, such as morbidity and mortality, are insufficient to improve the... View Details
Keywords:
Experience and Expertise;
Health Care and Treatment;
Medical Specialties;
Outcome or Result;
Performance Efficiency;
Performance Improvement
Carty, Matthew, MD, Rodney Chan, Robert S. Huckman, Daniel C. Snow, and Dennis Orgill. "A Detailed Analysis of the Reduction Mammaplasty Learning Curve: A Statistical Process Model for Approaching Surgical Performance Improvement." Plastic and Reconstructive Surgery 124, no. 3 (September 2009): 706–714.
- Forthcoming
- Article
Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals
By: Ta-Wei Huang and Eva Ascarza
Firms are increasingly interested in developing targeted interventions for customers with the best response,
which requires identifying differences in customer sensitivity, typically through the conditional average treatment
effect (CATE) estimation. In theory, to...
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Keywords:
Long-run Targeting;
Heterogeneous Treatment Effect;
Statistical Surrogacy;
Customer Churn;
Field Experiments;
Consumer Behavior;
Customer Focus and Relationships;
AI and Machine Learning;
Marketing Strategy
Huang, Ta-Wei, and Eva Ascarza. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals." Marketing Science (forthcoming). (Pre-published online March 6, 2024.)
- 2023
- Chapter
Marketing Through the Machine’s Eyes: Image Analytics and Interpretability
By: Shunyuan Zhang, Flora Feng and Kannan Srinivasan
he growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility—if only the...
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Zhang, Shunyuan, Flora Feng, and Kannan Srinivasan. "Marketing Through the Machine’s Eyes: Image Analytics and Interpretability." Chap. 8 in Artificial Intelligence in Marketing. 20, edited by Naresh K. Malhotra, K. Sudhir, and Olivier Toubia. Review of Marketing Research. Emerald Publishing Limited, forthcoming.
- Research Summary
Overview
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...
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- Research Summary
Overview
By: Iavor I. Bojinov
My research focuses on overcoming the methodological and operational challenges of developing data science capabilities, what I call data science operations. Today, within leading digital companies, data science is no longer confined to technical teams but is pervasive...
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- Research Summary
Overview
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...
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