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- March 2023
- Article
Attracting the Sharks: Corporate Innovation and Securities Class Action Lawsuits
By: Elisabeth Kempf and Oliver Spalt
This paper provides novel evidence suggesting that securities class action lawsuits, a central pillar of the U.S. litigation and corporate governance system, can constitute an obstacle to valuable corporate innovation. We first establish that valuable innovation output...
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- January 2023
- Case
First to Fight? Culture, Tradition and the United States Marine Corps (USMC)
By: Ranjay Gulati, Akhil Iyer and Joel Malkin
Over a history of more than 240 years, the United States Marine Corps has forged a distinct culture and institutional identity centered on its “warrior ethos.” In the wars of American history, Marines fought with uncommon valor, rising to international prominence for...
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- October 25, 2022
- Article
Why Sharing Economic Growth with the Community Is Good Business
By: José A. Tiburcio, Lino Miguel Dias and Robert S. Kaplan
Subsistence dairy ranchers in Central America struggle to stay afloat during the dry season when grass is scarce. Global life sciences company Bayer has launched a program to enable them to produce their own corn silage feed. The results of this program are helping to...
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- 2022
- Working Paper
Training, Communications Patterns, and Spillovers Inside Organizations
By: Miguel Espinosa and Christopher T. Stanton
We study direct productivity changes and spillovers after a randomized training program for the frontline workers in a Colombian government agency. While trained workers improved their individual production, we also find substantial spillovers that affected managers'...
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- 2022
- Article
Towards the Unification and Robustness of Post hoc Explanation Methods
By: Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu and Himabindu Lakkaraju
As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner. In this work, we analyze two...
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- 2022
- Article
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis.
By: Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay and Himabindu Lakkaraju
As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explanations in practice. Despite the growing popularity of counterfactual explanations, a...
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- Other Article
Sustainable Strategies and Net-Zero Goals
By: Mark L. Frigo, Robert S. Kaplan and Karthik Ramanna
In a recent Harvard Business Review article, Kaplan and Ramanna describe a rigorous approach, the E-liability method, for companies’ ESG reporting, especially as it pertains to GHG emissions measurements. They argue that the current standards for measuring...
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- 2022
- Working Paper
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
By: Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu and Himabindu Lakkaraju
As various post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to develop a deeper understanding of if and when the explanations output by these methods disagree with each other, and how...
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- January 2022
- Background Note
Common Prosperity? China Shifts Left
By: William C. Kirby and Noah B. Truwit
Since the founding of the People’s Republic of China in 1949, the ruling Chinese Communist Party (CCP) has been mistrustful of entrepreneurs and the private sector that operates outside the government’s authority. In its first decades under Mao Zedong, the CCP...
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Keywords:
Market Reform;
Gdp;
Government Administration;
Government and Politics;
Private Sector;
Governing Rules, Regulations, and Reforms;
Economy;
Globalized Economies and Regions;
Entrepreneurship;
Business and Government Relations;
Globalized Markets and Industries;
Social Issues;
Society;
Economic Growth;
China
- 2022
- White Paper
Building from the Bottom Up: What Business Can Do to Strengthen the Bottom Line by Investing in Front-line Workers
By: Joseph B. Fuller and Manjari Raman
A significant number of American workers—44%—are employed in low wage jobs at the front line of industries. Despite undertaking some of the most tedious, dirtiest, and most dangerous jobs, low-wage workers are—and have long been—the most likely to be overlooked by...
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- Article
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability
By: Dylan Slack, Sophie Hilgard, Sameer Singh and Himabindu Lakkaraju
As black box explanations are increasingly being employed to establish model credibility in high stakes settings, it is important to ensure that these explanations are accurate and reliable. However, prior work demonstrates that explanations generated by...
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- August 2021 (Revised February 2023)
- Case
Fair Park Covid-19 Mass Vaccination Site (A)
By: Willy Shih
This case is an opportunity to apply the methods of the Toyota Production System (TPS) to the analysis of an everyday service application: administering Covid-19 vaccines. It describes the start-up of a drive-up Covid-19 mass vaccination site at the Texas State Fair...
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- August 2021 (Revised July 2022)
- Supplement
Fair Park Covid-19 Mass Vaccination Site (B)
By: Willy Shih
This case is an opportunity to apply the methods of the Toyota Production System (TPS) to the analysis of an everyday service application: administering Covid-19 vaccines. It describes the start-up of a drive-up Covid-19 mass vaccination site at the Texas State Fair...
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- 2021
- Chapter
Towards a Unified Framework for Fair and Stable Graph Representation Learning
By: Chirag Agarwal, Himabindu Lakkaraju and Marinka Zitnik
As the representations output by Graph Neural Networks (GNNs) are increasingly employed in real-world applications, it becomes important to ensure that these representations are fair and stable. In this work, we establish a key connection between counterfactual...
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- Article
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
By: Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu and Himabindu Lakkaraju
As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner. In this work, we analyze two...
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- June 2021
- Technical Note
Introduction to Linear Regression
By: Michael Parzen and Paul Hamilton
This technical note introduces (from an applied point of view) the theory and application of simple and multiple linear regression. The motivation for the model is introduced, as well as how to interpret the summary output with regard to prediction and statistical...
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- May 2021
- Article
Fifty Shades of QE: Comparing Findings of Central Bankers and Academics
By: Brian Fabo, Marina Jančoková, Elisabeth Kempf and Ľuboš Pástor
We compare the findings of central bank researchers and academic economists regarding the macroeconomic effects of quantitative easing (QE). We find that central bank papers find QE to be more effective than academic papers do. Central bank papers report larger effects...
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- April 2021
- Article
Work-From-Anywhere: The Productivity Effects of Geographical Flexibility
By: Prithwiraj Choudhury, Cirrus Foroughi and Barbara Larson
An emerging form of remote work allows employees to work-from-anywhere, so that the worker can choose to live in a preferred geographic location. While traditional work-from-home (WFH) programs offer the worker temporal flexibility, work-from-anywhere (WFA) programs...
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- Mar 2021
- Conference Presentation
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
By: Seth Neel, Aaron Leon Roth and Saeed Sharifi-Malvajerdi
We study the data deletion problem for convex models. By leveraging techniques from convex optimization and reservoir sampling, we give the first data deletion algorithms that are able to handle an arbitrarily long sequence of adversarial updates while promising both...
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- Article
Internal Deadlines, Drug Approvals, and Safety Problems
By: Lauren Cohen, Umit Gurun and Danielle Li
Absent explicit quotas, incentives, reporting, or fiscal year-end motives, drug approvals around the world surge in December, at month-ends, and before respective major national holidays. Drugs approved before these informal deadlines are associated with significantly...
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