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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...  View Details
      Keywords: Class-action Litigation; Turnover; Lawsuits and Litigation; Innovation and Invention; Risk and Uncertainty
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      Kempf, Elisabeth, and Oliver Spalt. "Attracting the Sharks: Corporate Innovation and Securities Class Action Lawsuits." Management Science 69, no. 3 (March 2023): 1323–1934.
      • 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...  View Details
      Keywords: Change Management; Transformation; Talent and Talent Management; Government Administration; Management Practices and Processes; Management Systems; Organizational Change and Adaptation; Organizational Culture; Performance Effectiveness; United States
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      Gulati, Ranjay, Akhil Iyer, and Joel Malkin. "First to Fight? Culture, Tradition and the United States Marine Corps (USMC)." Harvard Business School Case 423-051, January 2023.
      • 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...  View Details
      Keywords: Sharing Economy; Innovation; Economic Growth; Poverty; Production; Supply Chain; Social Enterprise; Agriculture and Agribusiness Industry; Central America
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      Tiburcio, José A., Lino Miguel Dias, and Robert S. Kaplan. "Why Sharing Economic Growth with the Community Is Good Business." Harvard Business Review (website) (October 25, 2022).
      • 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'...  View Details
      Keywords: Spillovers; Labor Productivity; Firm Objectives, Organization, And Behavior; Training; Performance Productivity
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      Espinosa, Miguel, and Christopher T. Stanton. "Training, Communications Patterns, and Spillovers Inside Organizations." NBER Working Paper Series, No. 30224, July 2022.
      • 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...  View Details
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      Agarwal, Sushant, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, and Himabindu Lakkaraju. "Towards the Unification and Robustness of Post hoc Explanation Methods." Symposium on Foundations of Responsible Computing (FORC) (2022).
      • 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...  View Details
      Keywords: Machine Learning Models; Counterfactual Explanations; Adversarial Examples; Mathematical Methods
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      Pawelczyk, Martin, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, and Himabindu Lakkaraju. "Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 25th (2022).
      • 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...  View Details
      Keywords: Measurement; Sustainability; Net-zero Emissions; Environmental Sustainability; Integrated Corporate Reporting; Measurement and Metrics; Strategy
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      Frigo, Mark L., Robert S. Kaplan, and Karthik Ramanna. "Sustainable Strategies and Net-Zero Goals." Special Issue on Sustainability. Strategic Finance 103, no. 10 (April 2022): 42–49.
      • 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...  View Details
      Keywords: AI and Machine Learning; Analytics and Data Science; Mathematical Methods
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      Krishna, Satyapriya, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, and Himabindu Lakkaraju. "The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective." Working Paper, 2022.
      • 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...  View Details
      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
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      Kirby, William C., and Noah B. Truwit. "Common Prosperity? China Shifts Left." Harvard Business School Background Note 322-069, January 2022.
      • 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...  View Details
      Keywords: COVID-19; Labor Market; Low-wage Workers; Worker Welfare; Churn/retention; Morale; Jobs and Positions; Employees; Wages; Retention; Well-being; Human Resources
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      Fuller, Joseph B., and Manjari Raman. "Building from the Bottom Up: What Business Can Do to Strengthen the Bottom Line by Investing in Front-line Workers." White Paper, Harvard Business School, January 2022.
      • 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...  View Details
      Keywords: Black Box Explanations; Bayesian Modeling; Decision Making; Risk and Uncertainty; Information Technology
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      Slack, Dylan, Sophie Hilgard, Sameer Singh, and Himabindu Lakkaraju. "Reliable Post hoc Explanations: Modeling Uncertainty in Explainability." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
      • 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...  View Details
      Keywords: Operations Improvement; Operations And Processes; Toyota Production System; COVID-19 Pandemic; Operations; Service Operations; Health Pandemics; Problems and Challenges; Goals and Objectives; Measurement and Metrics; Health Care and Treatment; Health Industry; United States; Texas
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      Shih, Willy. "Fair Park Covid-19 Mass Vaccination Site (A)." Harvard Business School Case 622-003, August 2021. (Revised February 2023.)
      • 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...  View Details
      Keywords: Operations Management; Operations Improvement; Operations And Processes; Toyota Production System; COVID-19 Pandemic; Operations; Service Operations; Health Pandemics; Service Delivery; Problems and Challenges; Goals and Objectives; Measurement and Metrics; Health Industry; United States
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      Shih, Willy. "Fair Park Covid-19 Mass Vaccination Site (B)." Harvard Business School Supplement 622-004, August 2021. (Revised July 2022.)
      • 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...  View Details
      Keywords: Graph Neural Networks; AI and Machine Learning; Prejudice and Bias
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      Agarwal, Chirag, Himabindu Lakkaraju, and Marinka Zitnik. "Towards a Unified Framework for Fair and Stable Graph Representation Learning." In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence, edited by Cassio de Campos and Marloes H. Maathuis, 2114–2124. AUAI Press, 2021.
      • 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...  View Details
      Keywords: Machine Learning; Black Box Explanations; Decision Making; Forecasting and Prediction; Information Technology
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      Agarwal, Sushant, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, and Himabindu Lakkaraju. "Towards the Unification and Robustness of Perturbation and Gradient Based Explanations." Proceedings of the International Conference on Machine Learning (ICML) 38th (2021).
      • 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...  View Details
      Keywords: Linear Regression; Regression; Analysis; Forecasting and Prediction; Risk and Uncertainty; Theory; Compensation and Benefits; Mathematical Methods; Analytics and Data Science
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      Parzen, Michael, and Paul Hamilton. "Introduction to Linear Regression." Harvard Business School Technical Note 621-086, June 2021.
      • 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...  View Details
      Keywords: Quantitative Easing; Career Concerns; Economic Research; Central Banking; Macroeconomics; Economic Growth
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      Fabo, Brian, Marina Jančoková, Elisabeth Kempf, and Ľuboš Pástor. "Fifty Shades of QE: Comparing Findings of Central Bankers and Academics." Journal of Monetary Economics 120 (May 2021): 1–20.
      • 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...  View Details
      Keywords: Geographic Flexibility; Work-from-anywhere; Remote Work; Telecommuting; Geographic Mobility; USPTO; Employees; Geographic Location; Performance Productivity
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      Choudhury, Prithwiraj, Cirrus Foroughi, and Barbara Larson. "Work-From-Anywhere: The Productivity Effects of Geographical Flexibility." Strategic Management Journal 42, no. 4 (April 2021): 655–683.
      • 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...  View Details
      Keywords: Machine Learning; Unlearning Algorithm; Mathematical Methods
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      Neel, Seth, Aaron Leon Roth, and Saeed Sharifi-Malvajerdi. "Descent-to-Delete: Gradient-Based Methods for Machine Unlearning." Paper presented at the 32nd Algorithmic Learning Theory Conference, March 2021.
      • 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...  View Details
      Keywords: Health; Economics; Government and Politics; Innovation and Invention; Research; Science; Biotechnology Industry; Health Industry; Pharmaceutical Industry
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      Cohen, Lauren, Umit Gurun, and Danielle Li. "Internal Deadlines, Drug Approvals, and Safety Problems." American Economic Review: Insights 3, no. 1 (March 2021): 67–82.
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