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Show Results For
-
All HBS Web
(10,110)
- People (64)
- News (3,231)
- Research (3,925)
- Events (24)
- Multimedia (60)
- Faculty Publications (1,353)
- 01 Jun 2020
- News
Homeschooled
SK: I think it’s going to be a blend. We started the Khan Lab School because we believe so much in in-person instruction, especially with the younger age groups. In terms of higher education, by the time...
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- 2010
- Working Paper
Commodity Chains: What Can We Learn from a Business History of the Rubber Chain? (1870-1910)
By: Felipe Tamega Fernandes
The literature on the rubber boom applied a Dependendist view of rubber production in the Brazilian Amazon. Even though a sizable surplus was generated in the rubber chain, it was mostly appropriated by foreigners. This view is in tune with the Global Commodity Chain...
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Keywords:
Cross-Cultural and Cross-Border Issues;
Business History;
Supply Chain;
Manufacturing Industry;
Rubber Industry;
Brazil
Fernandes, Felipe Tamega. "Commodity Chains: What Can We Learn from a Business History of the Rubber Chain? (1870-1910)." Harvard Business School Working Paper, No. 10-089, April 2010.
- Research Summary
Paper - Commodity Chains: what can we learn from a business history of the rubber chain? (1870-1910)
The literature on the rubber boom applied a Marxist/Dependendist view of rubber production in the Brazilian Amazon. Even though a sizeable surplus was generated in the rubber chain, it was mostly appropriated by foreigners. This view is in tune with the Global... View Details
- 12 Oct 2021
- News
What Actually Draws Sports Fans to Games? It's Not Star Athletes.
- 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....
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Keywords:
Safety Regulations;
Regulations;
Regulatory Enforcement;
Machine Learning Models;
Safety;
Operations;
Service Operations;
Production;
Forecasting and Prediction;
Decisions;
United States
Johnson, Matthew S., David I. Levine, and Michael W. Toffel. "Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA." American Economic Journal: Applied Economics 15, no. 4 (October 2023): 30–67. (Profiled in the Regulatory Review.)
- 17 Nov 2020
External Partner Event: MBA Information Session Hosted by the HBS Club of the Philippines
Join the HBS Club of the Philippines to meet local alumni and learn about the value of Harvard Business School’s MBA. The event will run from 7:30pm-9:00pm Philippine Standard Time. This link goes directly to the Zoom room. The password...
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- 25 Jun 2019
- Blog Post
Learning the Language of Business and Science – The MS/MBA Biotechnology: Life Sciences Program
My path into the field of biotechnology began at a young age. I was largely influenced by my mother, who is a chemical engineer with an MBA, and by my upbringing in Boston, which exposed me to one of the...
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- 07 Jan 2015
- Research & Ideas
The Quest for Better Layoffs
A few years ago, Sandra J. Sucher received worried emails from two MBA students in her first-year Leadership and Corporate Accountability (LCA) class at Harvard Business School. Elana Green (now Elana Silver) and David Rosales (both HBS MBA 2010) had been troubled...
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- July 2017
- Teaching Note
Designing Transformational Customer Experiences
By: Stefan Thomke
Keywords:
Customer Experience;
Design;
Exercise;
Learning By Doing;
LEGO;
Storytelling;
Transformation
- 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...
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Slack, Dylan, Sophie Hilgard, Himabindu Lakkaraju, and Sameer Singh. "Counterfactual Explanations Can Be Manipulated." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- Article
Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error
By: Celia Cintas, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan and Edward McFowland III
Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving...
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Keywords:
Autoencoder Networks;
Pattern Detection;
Subset Scanning;
Computer Vision;
Statistical Methods And Machine Learning;
Machine Learning;
Deep Learning;
Data Mining;
Big Data;
Large-scale Systems;
Mathematical Methods;
Analytics and Data Science
Cintas, Celia, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan, and Edward McFowland III. "Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error." Proceedings of the International Joint Conference on Artificial Intelligence 29th (2020).
- 2021
- Working Paper
Time and the Value of Data
By: Ehsan Valavi, Joel Hestness, Newsha Ardalani and Marco Iansiti
Managers often believe that collecting more data will continually improve the accuracy of their machine learning models. However, we argue in this paper that when data lose relevance over time, it may be optimal to collect a limited amount of recent data instead of... View Details
Keywords:
Economics Of AI;
Machine Learning;
Non-stationarity;
Perishability;
Value Depreciation;
Analytics and Data Science;
Value
Valavi, Ehsan, Joel Hestness, Newsha Ardalani, and Marco Iansiti. "Time and the Value of Data." Harvard Business School Working Paper, No. 21-016, August 2020. (Revised November 2021.)
- 03 Mar 2023
- Research & Ideas
When Showing Know-How Backfires for Women Managers
Sometimes, trying to prove yourself in one task takes away time from doing other important tasks. “Women experience the fear that people are going to think they’re not good at, competent in, or capable in their roles.” Especially women...
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- 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...
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Keywords:
Machine Learning Models;
Algorithmic Recourse;
Decision Making;
Forecasting and Prediction
Upadhyay, Sohini, Shalmali Joshi, and Himabindu Lakkaraju. "Towards Robust and Reliable Algorithmic Recourse." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- 04 Oct 2010
- Research & Ideas
Introverts: The Best Leaders for Proactive Employees
relatively passive but the managers were extraverted. On the other hand, when employees were proactive, the stores led by introverted managers earned high profits. Meanwhile, profits were lower in stores where extraverted managers led...
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Keywords:
by Carmen Nobel
- 01 Dec 2019
- News
From Das’s Desk
As the External Relations team explores the breadth of options for lifelong learning content and programs for alumni, one area where we’re already seeing success is in the world of podcasting. Over the past few years, podcasts have...
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- 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...
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Keywords:
Machine Learning;
Econometric Analysis;
Instrumental Variable;
Random Forest;
Causal Inference;
AI and Machine Learning;
Forecasting and Prediction
Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
- 01 Dec 2013
- News
Recognizing Potential
When Mark Stevens (MBA 1989) first heard about HBX, he immediately recognized its potential for providing a transformational learning experience for participants around the world. "I understand the impact you can achieve View Details
- 09 Jun 2022
- HBS Case
From Truck Driver to Manager: US Foods’ Novel Approach to Staff Shortages
in March 2020, the pandemic only exacerbated a longstanding issue. The shortage of drivers to deliver food supplies to the roughly 300,000 restaurants, hotels, hospitals, schools, and universities serviced by US Foods was not its only...
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Keywords:
by Pamela Reynolds
- Article
Faithful and Customizable Explanations of Black Box Models
By: Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Jure Leskovec
As predictive models increasingly assist human experts (e.g., doctors) in day-to-day decision making, it is crucial for experts to be able to explore and understand how such models behave in different feature subspaces in order to know if and when to trust them. To...
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Lakkaraju, Himabindu, Ece Kamar, Rich Caruana, and Jure Leskovec. "Faithful and Customizable Explanations of Black Box Models." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2019).