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- 01 Sep 2011
- News
Capitalism’s False Mantra
of hedge funds, and encourage companies to contribute positively to society. The National Football League is Martin’s exemplar because it does all it can do to improve the customer experience while ensuring its employees — players to... View Details
- Article
Joy and Rigor in Behavioral Science
By: Hanne K. Collins, Ashley V. Whillans and Leslie K. John
In the past decade, behavioral science has seen the introduction of beneficial reforms to reduce false positive results. Serving as the motivational backdrop for the present research, we wondered whether these reforms might have unintended negative consequences on... View Details
Keywords: Open Science; Pre-registration; Exploration; Confirmation; False Positives; Career Satisfaction; Science; Research; Personal Development and Career; Satisfaction; Diversity
Collins, Hanne K., Ashley V. Whillans, and Leslie K. John. "Joy and Rigor in Behavioral Science." Organizational Behavior and Human Decision Processes 164 (May 2021): 179–191.
- August 2020 (Revised September 2020)
- Technical Note
Assessing Prediction Accuracy of Machine Learning Models
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools... View Details
Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods
Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
- 2023
- Working Paper
Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness
By: Neil Menghani, Edward McFowland III and Daniel B. Neill
In this paper, we develop a new criterion, "insufficiently justified disparate impact" (IJDI), for assessing whether recommendations (binarized predictions) made by an algorithmic decision support tool are fair. Our novel, utility-based IJDI criterion evaluates false... View Details
Menghani, Neil, Edward McFowland III, and Daniel B. Neill. "Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness." Working Paper, June 2023.
- 24 Aug 2021
- Cold Call Podcast
Why Did Pet Concierge Startup Baroo Fail?
- February 2021
- Tutorial
Assessing Prediction Accuracy of Machine Learning Models
By: Michael Toffel and Natalie Epstein
This video describes how to assess the accuracy of machine learning prediction models, primarily in the context of machine learning models that predict binary outcomes, such as logistic regression, random forest, or nearest neighbor models. After introducing and... View Details
- 05 Dec 2005
- What Do You Think?
Is Growth Good?
an important distinction: "Granted, the ethics of growth can be two-sided, but growth should not be confused with greed. Agriculture, healthcare, and education . . . are areas of potential growth that can be positive if not tainted... View Details
Keywords: by James Heskett
- 09 Nov 2017
- HBS Seminar
Alfonso Gambardella, Bocconi University
- July 2023 (Revised April 2024)
- Case
Raymond Jefferson: Trial by Fire
By: Anthony Mayo and Carin-Isabel Knoop
In the spring of 2021, Raymond (Ray) Jefferson applied for a job in President Joseph Biden’s administration. Ten years earlier, false allegations were used to force him to resign from his prior U.S. government position as Assistant Secretary of Labor for Veterans’... View Details
Mayo, Anthony, and Carin-Isabel Knoop. "Raymond Jefferson: Trial by Fire." Harvard Business School Case 423-094, July 2023. (Revised April 2024.)
- May–June 2021
- Article
Why Start-ups Fail
If you’re launching a business, the odds are against you: Two-thirds of start-ups never show a positive return. Unnerved by that statistic, a professor of entrepreneurship at Harvard Business School set out to discover why. Based on interviews and surveys with hundreds... View Details
Eisenmann, Thomas R. "Why Start-ups Fail." Harvard Business Review 99, no. 3 (May–June 2021): 76–85.
- 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.... View Details
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).
- 08 May 2025
- HBS Seminar
Ramesh Johari, Stanford
- Article
Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs
By: Michael G. Endres, Florian Hillen, Marios Salloumis, Ahmad R. Sedaghat, Stefan M. Niehues, Olivia Quatela, Henning Hanken, Ralf Smeets, Benedicta Beck-Broichsitter, Carsten Rendenbach, Karim R. Lakhani, Max Helland and Robert A. Gaudin
Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts, and tumors. In this study, we seek to investigate the... View Details
Keywords: Artificial Intelligence; Diagnosis; Computer-assisted; Image Interpretation; Machine Learning; Radiography; Panoramic Radiograph; AI and Machine Learning
Endres, Michael G., Florian Hillen, Marios Salloumis, Ahmad R. Sedaghat, Stefan M. Niehues, Olivia Quatela, Henning Hanken, Ralf Smeets, Benedicta Beck-Broichsitter, Carsten Rendenbach, Karim R. Lakhani, Max Helland, and Robert A. Gaudin. "Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs." Diagnostics 10, no. 6 (June 2020).
- 27 Feb 2012
- Research & Ideas
When Researchers Cheat (Just a Little)
other end of the spectrum, researchers can obtain false positives by running statistical tests over and over until they find the result they are looking for, or by excluding data after looking at how doing... View Details
- 14 Sep 2007
- Research & Ideas
How to Profit from Scarcity
the right place at the right price. Coca-Cola's mantra always has been to be within an arm's reach of desire. To be out of stock is to lose a sale or, worse, to lose a sale to a competitor. But marketers also understand that, by using the illusion of scarcity, they can... View Details
- Forthcoming
- Article
Slowly Varying Regression Under Sparsity
By: Dimitris Bertsimas, Vassilis Digalakis Jr, Michael Lingzhi Li and Omar Skali Lami
We consider the problem of parameter estimation in slowly varying regression models with sparsity constraints. We formulate the problem as a mixed integer optimization problem and demonstrate that it can be reformulated exactly as a binary convex optimization problem... View Details
Bertsimas, Dimitris, Vassilis Digalakis Jr, Michael Lingzhi Li, and Omar Skali Lami. "Slowly Varying Regression Under Sparsity." Operations Research (forthcoming). (Pre-published online March 27, 2024.)
- December 2024
- Article
Public Attitudes on Performance for Algorithmic and Human Decision-Makers
By: Kirk Bansak and Elisabeth Paulson
This study explores public preferences for algorithmic and human decision-makers (DMs) in high-stakes contexts, how these preferences are shaped by performance metrics, and whether public evaluations of performance differ depending on the type of DM. Leveraging a... View Details
Bansak, Kirk, and Elisabeth Paulson. "Public Attitudes on Performance for Algorithmic and Human Decision-Makers." PNAS Nexus 3, no. 12 (December 2024).
- January 2021
- Case
Anodot: Autonomous Business Monitoring
By: Antonio Moreno and Danielle Golan
Autonomous business monitoring platform Anodot leveraged machine learning to provide real-time alerts regarding business anomalies. Anodot’s solution was used in various industries in order to primarily monitor business health, such as revenue and payments, product... View Details
Keywords: Digital Platforms; Internet and the Web; Knowledge Sharing; Information Management; Sales; Value Creation; Product Positioning; Israel
Moreno, Antonio, and Danielle Golan. "Anodot: Autonomous Business Monitoring." Harvard Business School Case 621-084, January 2021.
- 2019
- Article
An Empirical Study of Rich Subgroup Fairness for Machine Learning
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
Kearns et al. [2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across... View Details
Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "An Empirical Study of Rich Subgroup Fairness for Machine Learning." Proceedings of the Conference on Fairness, Accountability, and Transparency (2019): 100–109.