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- April 2025
- Background Note
Customer Acquisition and the Cash Flow Trap
By: E. Ofek, Barak Libai and Eitan Muller
Startups as well as existing firms recognize the need to invest in order to acquire customers for their new ventures. And as each customer is expected at some point to have generated sufficient gross margins to cover their CAC, management expects that, soon enough, the... View Details
- 2024
- Working Paper
The Narrative AI Advantage? A Field Experiment on Generative AI-Augmented Evaluations of Early-Stage Innovations
By: Jacqueline N. Lane, Léonard Boussioux, Charles Ayoubi, Ying Hao Chen, Camila Lin, Rebecca Spens, Pooja Wagh and Pei-Hsin Wang
The rise of generative artificial intelligence (AI) is transforming creative problem-solving, necessitating new approaches for evaluating innovative solutions. This study explores how human-AI collaboration can enhance early-stage evaluations, focusing on the interplay... View Details
- 2024
- Article
A Universal In-Place Reconfiguration Algorithm for Sliding Cube-Shaped Robots in Quadratic Time
By: Zachary Abel, Hugo A. Akitaya, Scott Duke Kominers, Matias Korman and Frederick Stock
In the modular robot reconfiguration problem we are given n cube-shaped modules (or "robots") as well as two configurations, i.e., placements of the n modules so that their union is face-connected. The goal is to find a sequence of moves that reconfigures the modules... View Details
- February 2024
- Article
Representation and Extrapolation: Evidence from Clinical Trials
By: Marcella Alsan, Maya Durvasula, Harsh Gupta, Joshua Schwartzstein and Heidi L. Williams
This article examines the consequences and causes of low enrollment of Black patients in clinical
trials. We develop a simple model of similarity-based extrapolation that predicts that evidence is
more relevant for decision-making by physicians and patients when it... View Details
- June 2023 (Revised September 2023)
- Simulation
Managing the Customer Journey Marketing Simulation: Adobe's Data-Driven Operating Model (DDOM)
By: Sunil Gupta, Rajiv Lal and Celine Chammas
Adobe started monitoring Annual Recurring Revenue (ARR), one of its primary metrics, when it shifted from selling its software in a box to selling the software as a subscription-based cloud service. They wanted to know when, where, and how much to invest in marketing.... View Details
- July 2022 (Revised February 2025)
- Case
A Soul and a Service: North Carolina Mutual Life Insurance
By: Tom Nicholas and John Masko
The North Carolina Mutual and Provident Association (the Mutual) was founded in 1898 as a for-profit entity selling life insurance catering to the Black community. The Mutual was entering a field crowded with established White-owned competitors that largely refused to... View Details
- 2022
- Conference Presentation
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
Keywords: AI and Machine Learning
- January 2022 (Revised August 2022)
- Case
Jackie Robinson: Changing the World
By: Robert Simons and Max Saffer
This case traces the rise of Jackie Robinson from the poor streets of Pasadena, California to one of the most famous people in America after he overturned the color barrier in baseball. The case describes how as a youth he excelled at basketball, football, baseball,... View Details
- 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
- September 17, 2021
- Article
AI Can Help Address Inequity—If Companies Earn Users' Trust
By: Shunyuan Zhang, Kannan Srinivasan, Param Singh and Nitin Mehta
While companies may spend a lot of time testing models before launch, many spend too little time considering how they will work in the wild. In particular, they fail to fully consider how rates of adoption can warp developers’ intent. For instance, Airbnb launched a... View Details
- 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
- April 2021
- Case
Glass-Shattering Leaders: Michele Hooper
By: Boris Groysberg and Colleen Ammerman
Michele Hooper joined the board of the Dayton-Hudson Corporation when she was in her late thirties, becoming the company’s youngest director as well as the only woman and the only person of color in the boardroom. Such “firsts” were not unusual for Hooper, who had been... View Details
- February 2021 (Revised October 2024)
- Case
Muhammad Ali: Changing The World
By: Robert Simons and Max Saffer
This case describes the rise of Cassius Clay, who later called himself Muhammad Ali, from the poor streets of Louisville, Kentucky to international fame. The case describes how Ali won a gold medal in the Olympics, three heavyweight boxing titles, and became a role... 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
- Article
Robust and Stable Black Box Explanations
By: Himabindu Lakkaraju, Nino Arsov and Osbert Bastani
As machine learning black boxes are increasingly being deployed in real-world applications, there
has been a growing interest in developing post hoc explanations that summarize the behaviors
of these black boxes. However, existing algorithms for generating such... View Details
- 2020
- Article
'How Do I Fool You?': Manipulating User Trust via Misleading Black Box Explanations
By: Himabindu Lakkaraju and Osbert Bastani
- 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
- March 2018
- Case
EKOL Logistics: Thinking Outside the Box
By: Willy C. Shih and Esel Çekin
This case describes Ekol, an intermodal transportation and logistics company, and how it manages capacity planning. Its busiest routes linked motor vehicle assemblers in Germany and Turkey with many of their parts suppliers, but it had also developed key links in... View Details
Keywords: Growth And Development; Strategy; Intermodal Transportation; Short-sea Transportation; Capacity Management; Capacity Planning; Delivery Planning; Route Optimization; Car Spare Part; Auto Manufacturing; Automotive Supply Chain; Europe; Turkey; Service Design; Fast Fashion; Near-shoring; Supply Chain; Supply Chain Management; Operations; Performance Capacity; Performance Efficiency; Logistics; Transportation Industry; Auto Industry; Turkey; Germany; Spain; European Union; Europe
- August 2017 (Revised July 2018)
- Case
MannKind Corporation: Take a Deep Breath, This Time Afrezza Will Work
By: Elie Ofek and Amanda Dai
In June 2014, MannKind Corporation announced that after years of development and billions of dollars in expenses, the FDA had finally approved its drug, Afrezza. MannKind would thus be the only company with an inhalable insulin on the market. As an alternative to... View Details
- 2015
- Chapter
Optimal Process Control of Symbolic Transfer Functions
By: Christopher Griffin and Elisabeth Paulson
Transfer function modeling is a standard technique in classical Linear Time Invariant and Statistical Process Control. The work of Box and Jenkins was seminal in developing methods for identifying parameters associated with classical (r, s, k) transfer functions.... View Details