Filter Results:
(10,177)
Show Results For
- All HBS Web
(10,177)
- People (64)
- News (3,258)
- Research (3,953)
- Events (24)
- Multimedia (60)
- Faculty Publications (1,368)
Show Results For
- All HBS Web
(10,177)
- People (64)
- News (3,258)
- Research (3,953)
- Events (24)
- Multimedia (60)
- Faculty Publications (1,368)
- 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.... View Details
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.)
- 1995
- Article
The Positive Impact of Creative Activity: Effects of Creative Task Engagement and Motivational Focus on College Student's Learning
By: R. Conti, T. M. Amabile and S. Pollack
This study assessed the effectiveness of engaging students in a creative activity on a topic as a means of encouraging an active cognitive set toward learning that topic area. This technique was examined in three motivational contexts. Before reading a short... View Details
Keywords: Creativity; Cognition and Thinking; Behavior; Performance; Motivation and Incentives; Training
Conti, R., T. M. Amabile, and S. Pollack. "The Positive Impact of Creative Activity: Effects of Creative Task Engagement and Motivational Focus on College Student's Learning." Personality and Social Psychology Bulletin 21 (1995): 1107–1116.
- 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... View Details
- 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... View Details
- 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... View Details
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).
- 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... View Details
- 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... View Details
Slack, Dylan, Sophie Hilgard, Himabindu Lakkaraju, and Sameer Singh. "Counterfactual Explanations Can Be Manipulated." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- 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.)
- 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
- 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... View Details
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).
- 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... View Details
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).
- 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
- 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... View Details
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.
- September 2018 (Revised December 2019)
- Case
Zebra Medical Vision
By: Shane Greenstein and Sarah Gulick
An Israeli startup founded in 2014, Zebra Medical Vision developed algorithms that produced diagnoses from X-rays, mammograms, and CT-scans. The algorithms used deep learning and digitized radiology scans to create software that could assist doctors in making... View Details
Keywords: Radiology; Machine Learning; X-ray; CT Scan; Medical Technology; Probability; FDA 510(k); Diagnosis; Business Startups; Health Care and Treatment; Information Technology; Applications and Software; Competitive Strategy; Product Development; Commercialization; Decision Choices and Conditions; Health Industry; Medical Devices and Supplies Industry; Technology Industry; Israel
Greenstein, Shane, and Sarah Gulick. "Zebra Medical Vision." Harvard Business School Case 619-014, September 2018. (Revised December 2019.)
- Article
Repositioning and Cost-Cutting: The Impact of Competition on Platform Strategies
By: Robert Seamans and Feng Zhu
Organizational structures are increasingly complex. In particular, more firms today operate as multi-sided platforms. In this paper, we study how platform firms use repositioning and cost-cutting in response to competition, elucidate external and internal factors that... View Details
Keywords: Platform Strategy; Repositioning; Cost-cutting; Intra-firm Learning; Multi-Sided Platforms; Cost Management; Product Positioning; Organizational Structure; Competitive Strategy; Knowledge Acquisition; Journalism and News Industry
Seamans, Robert, and Feng Zhu. "Repositioning and Cost-Cutting: The Impact of Competition on Platform Strategies." Strategy Science 2, no. 2 (June 2017): 83–99.
- April 2017
- Case
The Future of Patent Examination at the USPTO
By: Prithwiraj Choudhury, Tarun Khanna and Sarah Mehta
The U.S. Patent and Trademark Office (USPTO) is the federal government agency responsible for evaluating and granting patents and trademarks. In 2015, the USPTO employed approximately 8,000 patent examiners who granted nearly 300,000 patents to inventors. As of April... View Details
Keywords: Machine Learning; Telework; Collaborating With Unions; Human Resources; Recruitment; Retention; Intellectual Property; Copyright; Patents; Trademarks; Knowledge Sharing; Technology Adoption; Organizational Change and Adaptation; Performance Productivity; Performance Improvement; District of Columbia
Choudhury, Prithwiraj, Tarun Khanna, and Sarah Mehta. "The Future of Patent Examination at the USPTO." Harvard Business School Case 617-027, April 2017.
- Summer 2020
- Article
Venture Capital's Role in Financing Innovation: What We Know and How Much We Still Need to Learn
By: Josh Lerner and Ramana Nanda
Venture capital is associated with some of the most high-growth and influential firms in the world. Academics and practitioners have effectively articulated the strengths of the venture model. At the same time, venture capital financing also has real limitations in its... View Details
Lerner, Josh, and Ramana Nanda. "Venture Capital's Role in Financing Innovation: What We Know and How Much We Still Need to Learn." Journal of Economic Perspectives 34, no. 3 (Summer 2020): 237–261.
- 2021
- Working Paper
First Law of Motion: Influencer Video Advertising on TikTok
By: Jeremy Yang, Juanjuan Zhang and Yuhan Zhang
This paper engineers an intuitive feature that is predictive of the causal effect of influencer video advertising on product sales. We propose the concept of m-score, a summary statistic that captures the extent to which a product is advertised in the most engaging... View Details
Keywords: Influencer Advertising; Video Advertising; Computer Vision; Machine Learning; Advertising; Online Technology
Yang, Jeremy, Juanjuan Zhang, and Yuhan Zhang. "First Law of Motion: Influencer Video Advertising on TikTok." Working Paper, March 2021.
- 2017
- Working Paper
Discretionary Task Ordering: Queue Management in Radiological Services
By: Maria Ibanez, Jonathan R. Clark, Robert S. Huckman and Bradley R. Staats
Work scheduling research typically prescribes task sequences implemented by managers. Yet employees often have discretion to deviate from their prescribed sequence. Using data from 2.4 million radiological diagnoses, we find that doctors prioritize similar tasks... View Details
Keywords: Discretion; Scheduling; Queue; Healthcare; Learning; Experience; Decentralization; Delegation; Behavioral Operations; Operations; Service Operations; Service Delivery; Performance; Performance Effectiveness; Performance Efficiency; Performance Improvement; Performance Productivity; Decisions; Time Management; Cost vs Benefits; Health Industry
Ibanez, Maria, Jonathan R. Clark, Robert S. Huckman, and Bradley R. Staats. "Discretionary Task Ordering: Queue Management in Radiological Services." Harvard Business School Working Paper, No. 16-051, October 2015. (Revised March 2017.)