Publications
Publications
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- All HBS Web
(1,189)
- Faculty Publications (351)
- 2021
- Working Paper
Quantifying the Value of Iterative Experimentation
By: Iavor I Bojinov and Jialiang Mao
Over the past decade, most technology companies and a growing number of conventional firms have adopted online experimentation (or A/B testing) into their product development process. Initially, A/B testing was deployed as a static procedure in which an experiment was... View Details
- March 2024
- Teaching Note
Experimentation at Yelp
By: Iavor Bojinov and Jessie Li
Teaching Note for HBS Case No. 621-064. View Details
- February 26, 2024
- Article
Making Workplaces Safer Through Machine Learning
By: Matthew S. Johnson, David I. Levine and Michael W. Toffel
Machine learning algorithms can dramatically improve regulatory effectiveness. This short article describes the authors' scholarly work that shows how the U.S. Occupational Safety and Health Administration (OSHA) could have reduced nearly twice as many occupational... View Details
- February 2024
- Article
Conveying and Detecting Listening in Live Conversation
By: Hanne Collins, Julia A. Minson, Ariella S. Kristal and Alison Wood Brooks
Across all domains of human social life, positive perceptions of conversational listening (i.e., feeling heard) predict well-being, professional success, and interpersonal flourishing. But a fundamental question remains: Are perceptions of listening accurate? Prior... View Details
- January 2024
- Article
Population Interference in Panel Experiments
By: Kevin Wu Han, Guillaume Basse and Iavor Bojinov
The phenomenon of population interference, where a treatment assigned to one experimental unit affects another experimental unit’s outcome, has received considerable attention in standard randomized experiments. The complications produced by population interference in... View Details
- 2023
- Article
Balancing Risk and Reward: An Automated Phased Release Strategy
By: Yufan Li, Jialiang Mao and Iavor Bojinov
Phased releases are a common strategy in the technology industry for gradually releasing new products or updates through a sequence of A/B tests in which the number of treated units gradually grows until full deployment or deprecation. Performing phased releases in a... View Details
- 2023
- Working Paper
Complexity and Hyperbolic Discounting
By: Benjamin Enke, Thomas Graeber and Ryan Oprea
A large literature shows that people discount financial rewards hyperbolically instead of exponentially. While discounting of money has been questioned as a measure of time preferences, it continues to be highly relevant in empirical practice and predicts a wide range... View Details
- 2023
- Chapter
Malleability Interventions in Intergroup Relations
By: Smadar Cohen-Chen, Amit Goldenberg, James J. Gross and Eran Halperin
One important characteristic of intergroup relations and conflicts is the fact that toxic or violent intergroup relations are often associated with fixed and stable perceptions of various entities, including the ingroup (stable and positive), the outgroup (stable and... View Details
- 2023
- Article
Post Hoc Explanations of Language Models Can Improve Language Models
By: Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh and Himabindu Lakkaraju
Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks. Moreover, recent research has shown that incorporating human-annotated rationales (e.g., Chain-of-Thought prompting) during in-context learning can significantly enhance... View Details
- 2023
- Working Paper
In-Context Unlearning: Language Models as Few Shot Unlearners
By: Martin Pawelczyk, Seth Neel and Himabindu Lakkaraju
Machine unlearning, the study of efficiently removing the impact of specific training points on the
trained model, has garnered increased attention of late, driven by the need to comply with privacy
regulations like the Right to be Forgotten. Although unlearning is... View Details
- 2023
- Working Paper
Words Can Hurt: How Political Communication Can Change the Pace of an Epidemic
By: Jessica Gagete-Miranda, Lucas Argentieri Mariani and Paula Rettl
While elite-cue effects on public opinion are well-documented, questions remain as
to when and why voters use elite cues to inform their opinions and behaviors. Using
experimental and observational data from Brazil during the COVID-19 pandemic, we
study how leader... View Details
Keywords: Elites; Public Engagement; Politics; Political Affiliation; Political Campaigns; Political Influence; Political Leadership; Political Economy; Survey Research; COVID-19; COVID-19 Pandemic; COVID; Cognitive Psychology; Cognitive Biases; Political Elections; Voting; Power and Influence; Identity; Behavior; Latin America; Brazil
- 2023
- Working Paper
Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality
By: Fabrizio Dell'Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon and Karim R. Lakhani
The public release of Large Language Models (LLMs) has sparked tremendous interest in how humans will use Artificial Intelligence (AI) to accomplish a variety of tasks. In our study conducted with Boston Consulting Group, a global management consulting firm, we examine... View Details
- September 2023
- Article
Addressing Vaccine Hesitancy: Experimental Evidence from Nine Countries during the COVID-19 Pandemic
By: Vincenzo Galasso, Vincent Pons, Paola Profeta, Martin McKee, David Stuckler, Michael Becher, Sylvain Brouard and Martial Foucault
We study the impact of public health messages on intentions to vaccinate and vaccination uptakes, especially among hesitant groups. We performed an experiment comparing the effects of egoistic and altruistic messages on COVID-19 vaccine intentions and behaviour. We... View Details
- 2023
- Article
On Minimizing the Impact of Dataset Shifts on Actionable Explanations
By: Anna P. Meyer, Dan Ley, Suraj Srinivas and Himabindu Lakkaraju
The Right to Explanation is an important regulatory principle that allows individuals to request actionable explanations for algorithmic decisions. However, several technical challenges arise when providing such actionable explanations in practice. For instance, models... View Details
- 2023
- Article
On the Impact of Actionable Explanations on Social Segregation
By: Ruijiang Gao and Himabindu Lakkaraju
As predictive models seep into several real-world applications, it has become critical to ensure that individuals who are negatively impacted by the outcomes of these models are provided with a means for recourse. To this end, there has been a growing body of research... View Details
- August 2023
- Case
Reimagining Hindustan Unilever (A)
By: Sunil Gupta and Rachna Tahilyani
In the fall of 2019, the CEO and MD of Hindustan Unilever (HUL), India’s largest fast-moving consumer goods (FMCG) firm, is wondering what to do about their experiments to digitize distribution. Despite three years of intense efforts, their apps to empower retailers... View Details
- August 2023
- Supplement
Reimagining Hindustan Unilever (B)
By: Sunil Gupta and Rachna Tahilyani
In April 2023, as the CEO and MD of Hindustan Unilever (HUL), India’s largest fast-moving consumer goods (FMCG) firm, prepared to hand over the firm’s reins to his successor, he proudly reflected on the last decade. His quest to digitally transform HUL into an... View Details
- 2023
- Article
Towards Bridging the Gaps between the Right to Explanation and the Right to Be Forgotten
By: Himabindu Lakkaraju, Satyapriya Krishna and Jiaqi Ma
The Right to Explanation and the Right to be Forgotten are two important principles outlined to regulate algorithmic decision making and data usage in real-world applications. While the right to explanation allows individuals to request an actionable explanation for an... View Details
- 18 Jul 2023
- Interview
Jeffrey Rayport on Product Market Fit, Profit Market Fit and Whiplash, and More
By: Jeffrey F. Rayport and Doug Levin
This episode of "Lessons from Startup Life" podcast features Jeffrey Rayport, Senior Lecturer of Business Administration at the Harvard Business School. Jeffrey specializes in teaching and researching growth-stage technology ventures and their scalability. Prior to... View Details