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- 2025
- Working Paper
Global Evidence on Gender Gaps and Generative AI
By: Nicholas G. Otis, Solène Delecourt, Katelynn Cranney and Rembrand Koning
Generative AI has the potential to transform productivity and reduce inequality, but only if adopted broadly. In this paper, we show that recently identified gender gaps in generative AI use are nearly universal. Synthesizing data from 18 studies covering more than... View Details
- July 2024
- Article
How Artificial Intelligence Constrains Human Experience
By: A. Valenzuela, S. Puntoni, D. Hoffman, N. Castelo, J. De Freitas, B. Dietvorst, C. Hildebrand, Y.E. Huh, R. Meyer, M. Sweeney, S. Talaifar, G. Tomaino and K. Wertenbroch
Many consumption decisions and experiences are digitally mediated. As a consequence, consumer behavior is increasingly the joint product of human psychology and ubiquitous algorithms (Braun et al. 2024; cf. Melumad et al. 2020). The coming of age of Large Language... View Details
- 2024
- Working Paper
Webmunk: A New Tool for Studying Online Behavior and Digital Platforms
By: Chiara Farronato, Andrey Fradkin and Chris Karr
Understanding the behavior of users online is important for researchers, policymakers, and private companies alike. But observing online behavior and conducting experiments is difficult without direct access to the user base and software of technology companies. We... View Details
- 2024
- Working Paper
AI Companions Reduce Loneliness
By: Julian De Freitas, Ahmet K. Uğuralp, Zeliha O. Uğuralp and Stefano Puntoni
Chatbots are now able to engage in sophisticated conversations with consumers in the domain of relationships, providing a potential coping solution to widescale societal loneliness. Behavioral research provides little insight into whether these applications are... View Details
- June 2024
- Article
Valuing the Societal Impact of Medicines and Other Health Technologies: A User Guide to Current Best Practices
By: Jason Shafrin, Jaehong Kim, Joshua T. Cohen, Louis P. Garrison, Dana A. Goldman, Jalpa A. Doshi, Joshua Krieger, Darius N. Lakdawalla, Peter J. Neumann, Charles E. Phelps, Melanie D. Whittington and Richard Willke
This study argues that value assessment conducted from a societal perspective should rely on the Generalized Cost-Effectiveness Analysis (GCEA) framework proposed herein. Recently developed value assessment inventories—such as the Second Panel on Cost-Effectiveness’s... View Details
- 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
- February 2024 (Revised January 2025)
- Supplement
Time to Play? User Research Exercise
- 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
- July 2023
- Case
DayTwo: Going to Market with Gut Microbiome (Abridged)
By: Ayelet Israeli
DayTwo is a young Israeli startup that applies research on the gut microbiome and machine learning algorithms to deliver personalized nutritional recommendations to its users in order to minimize blood sugar spikes after meals. After a first year of trial rollout in... View Details
- July 2023
- Article
Negative Expressions Are Shared More on Twitter for Public Figures Than for Ordinary Users
By: Jonas P. Schöne, David Garcia, Brian Parkinson and Amit Goldenberg
Social media users tend to produce content that contains more positive than negative emotional language. However, negative emotional language is more likely to be shared. To understand why, research has thus far focused on psychological processes associated with... View Details
- 2023
- Article
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju
As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely impacted (e.g., loan denied) by the predictions of these models are provided with a means... View Details
- April 19, 2023
- Editorial
Extreme Views Are More Attractive Than Moderate Ones
By: Amit Goldenberg
Do you ever feel like everyone on social media has a more extreme viewpoint than your own? We often blame social media companies for the cacophony of politically extreme opinions around us. After all, these companies are generally motivated to promote the most... View Details
- November 2022 (Revised March 2024)
- Case
Replika AI: Monetizing a Chatbot
By: Julian De Freitas and Nicole Tempest Keller
In early 2018, Eugenia Kuyda, co-founder and CEO of San Francisco-based chatbot Replika AI, was deciding how to monetize the app she had built. Launched in 2017, Replika was a consumer AI “companion app” developed by a team of AI software engineers originally based in... View Details
- 2022
- Article
Towards Robust Off-Policy Evaluation via Human Inputs
By: Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez and Himabindu Lakkaraju
Off-policy Evaluation (OPE) methods are crucial tools for evaluating policies in high-stakes domains such as healthcare, where direct deployment is often infeasible, unethical, or expensive. When deployment environments are expected to undergo changes (that is, dataset... View Details
- 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
- 2022
- Working Paper
TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations
By: Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju and Sameer Singh
Practitioners increasingly use machine learning (ML) models, yet they have become more complex and harder to understand. To address this issue, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use explainability... View Details
- Article
The Translucent Hand of Managed Ecosystems: Engaging Communities for Value Creation and Capture
By: Elizabeth J. Altman, Frank Nagle and Michael Tushman
Management research has increasingly explored the domains of ecosystems, platforms, and open/user/distributed innovation—governance structures focused on engaging with external communities. While these research areas include substantial empirical and theoretical work... View Details
- December 2021
- Article
Left- and Right-Leaning News Organizations Use Negative Emotional Content and Elicit User Engagement Similarly
By: Andrea Bellovary, Nathaniel Young and Amit Goldenberg
Negativity has historically dominated news content; however, little research has examined how news organizations use affect on social media, where content is generally positive. In the current project we ask a few questions: Do news organizations on Twitter use... View Details
Keywords: Negative Press; Twitter; Political Affiliation; Affect; News; Media; Internet and the Web; Emotions; Perspective; Social Media
- 2021
- Article
Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring
By: Tom Sühr, Sophie Hilgard and Himabindu Lakkaraju
Ranking algorithms are being widely employed in various online hiring platforms including LinkedIn, TaskRabbit, and Fiverr. Prior research has demonstrated that ranking algorithms employed by these platforms are prone to a variety of undesirable biases, leading to the... View Details
- Article
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses
By: Kaivalya Rawal and Himabindu Lakkaraju
As predictive models are increasingly being deployed in high-stakes decision-making, there has been a lot of interest in developing algorithms which can provide recourses to affected individuals. While developing such tools is important, it is even more critical to... View Details