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Publications
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- Working Paper
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...
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- July 2022
- 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...
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- 2022
- Article
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...
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- 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,...
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- 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...
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- October 2021
- Article
Judgment Aggregation in Creative Production: Evidence from the Movie Industry
By: Hong Luo, Jeffrey T. Macher and Michael Wahlen
We study a novel, low-cost approach to aggregating judgment from a large number of industry experts on ideas that they encounter in their normal course of business. Our context is the movie industry, in which customer appeal is difficult to predict and investment costs...
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- 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...
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- 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...
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- 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...
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- February 2021 (Revised January 2022)
- 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...
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- 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...
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- 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...
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- 2021
- Working Paper
Hunting for Talent: Firm-driven Labor Market Search in the United States
By: Rembrand Koning, Sharique Hasan and Ines Black
This article analyzes the phenomenon of firm-driven labor market search—or outbound recruiting—where recruiters are increasingly “hunting for talent” rather than passively relying on workers to search for and apply to job vacancies. Our research methodology leverages...
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- 2020
- Article
'How Do I Fool You?': Manipulating User Trust via Misleading Black Box Explanations
By: Himabindu Lakkaraju and Osbert Bastani
- 2019
- Working Paper
Judgment Aggregation in Creative Production: Evidence from the Movie Industry
By: Hong Luo, Jeffrey T. Macher and Michael Wahlen
This paper studies a novel, light-touch approach to aggregate judgment from a large number of industry experts on ideas that they encounter in their normal course of business. Our context is the movie industry, in which customer appeal is difficult to predict and...
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- 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...
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- 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...
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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...
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- February 2015
- Other Article
Evaluating the Impact of the Baby-Friendly Hospital Initiative on Breast-feeding Rates: A Multi-state Analysis
By: Summer Sherburne Hawkins, Ariel Dora Stern, Christopher F. Baum and Matthew W. Gillman
Objectives: Despite the passage of state laws promoting breast feeding, a formal evaluation has not yet been conducted to test whether and/or what type of laws may increase breast feeding. The enactment of breastfeeding laws in different states in the USA creates a...
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- 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....
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