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Show Results For
- All HBS Web
(10,855)
- People (64)
- News (3,423)
- Research (3,988)
- Events (30)
- Multimedia (61)
- Faculty Publications (1,451)
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- 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).
- 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... View Details
Keywords: by Pamela Reynolds
- 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).
- 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).
- 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.)
- 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... View Details
- 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.)
- 27 Sep 2021
- Research & Ideas
Managers, Your Employees Don’t Want to Be Facebook ‘Friends’
Online—was coauthored by Nancy P. Rothbard, David Pottruck Professor of Management of the Wharton School of Business; Lakshmi Ramarajan, the Anna Spangler Nelson and Thomas C. Nelson Associate Professor of Business Administration at... View Details
Keywords: by Rachel Kim Raczka
- 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.
- 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... View Details
- 29 Jul 2002
- Research & Ideas
Time Pressure and Creativity: Why Time is Not on Your Side
need to keep creative thinking in their organizations even as time pressures increase. Silverthorne: What was the genesis of the project? What fascinated you about the question of time pressure and creativity? Amabile: Over the course of... View Details
- 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.
- 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).
- 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.
- 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... View Details
Keywords: by Carmen Nobel
- 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.
- 19 Jan 2015
- Research & Ideas
Is Wikipedia More Biased Than Encyclopædia Britannica?
institution announced it would no longer publish a print version of its multivolume compendium of knowledge. Though the Britannica would still be available online, the writing on the virtual wall was clear: It had been supplanted by the... View Details
- Article
The Feeling of Not Knowing It All
By: Haiyang Yang, Ziv Carmon, Dan Ariely and Michael I. Norton
How do consumers assess their mastery of knowledge they have learned? We explore this question by investigating a common knowledge consumption situation: encountering opportunities for further learning. We argue and show that such opportunities can trigger a... View Details
Keywords: Knowledge Consumption; Consumption Of Learning; Judgment Of Knowledge; Feeling Ofknowing; Confidence In Knowledge; WYSIATI; FONKIA; Knowledge Acquisition; Learning; Perception
Yang, Haiyang, Ziv Carmon, Dan Ariely, and Michael I. Norton. "The Feeling of Not Knowing It All." Journal of Consumer Psychology 29, no. 3 (July 2019): 455–462.
- 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.)