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-
All HBS Web
(811)
- People (1)
- News (149)
- Research (501)
- Events (7)
- Multimedia (4)
- Faculty Publications (406)
- 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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Keywords:
Machine Learning;
Black Box Explanations;
Decision Making;
Forecasting and Prediction;
Information Technology
Agarwal, Sushant, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, and Himabindu Lakkaraju. "Towards the Unification and Robustness of Perturbation and Gradient Based Explanations." Proceedings of the International Conference on Machine Learning (ICML) 38th (2021).
- 2021
- Conference Presentation
An Algorithmic Framework for Fairness Elicitation
By: Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Leon Roth, Logan Stapleton and Zhiwei Steven Wu
We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more complex and nuanced and thus require elicitation from individual or collective stakeholders....
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Jung, Christopher, Michael J. Kearns, Seth Neel, Aaron Leon Roth, Logan Stapleton, and Zhiwei Steven Wu. "An Algorithmic Framework for Fairness Elicitation." Paper presented at the 2nd Symposium on Foundations of Responsible Computing (FORC), 2021.
- February 2019
- Case
Miroglio Fashion (A)
By: Sunil Gupta and David Lane
Francesco Cavarero, chief information officer of Miroglio Fashion, Italy’s third-largest retailer of women’s apparel, was trying to bring analytical rigor to the company’s forecasting and inventory management decisions. But fashion is inherently hard to predict. Can...
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Keywords:
Inventory Management;
Demand Forecasting;
Artificial Intelligence;
Machine Learning;
Forecasting and Prediction;
Operations;
Management;
Decision Making;
AI and Machine Learning;
Apparel and Accessories Industry;
Fashion Industry
Gupta, Sunil, and David Lane. "Miroglio Fashion (A)." Harvard Business School Case 519-053, February 2019.
- 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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Lakkaraju, Himabindu, Nino Arsov, and Osbert Bastani. "Robust and Stable Black Box Explanations." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020): 5628–5638. (Published in PMLR, Vol. 119.)
- 18 Apr 2000
- Research & Ideas
Learning in Action
"The most effective learning strategy depends on the situation," writes David A. Garvin. "There is no stock answer, nor is there a single best approach." In Learning in Action, he illustrated the diversity...
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Keywords:
by David A. Garvin
- 29 Sep 2020
Life at HBS Chat Series: MBA Students in Tech Club and Coding, Analytics, and Machine Learning Club
Hear straight from current HBS students regarding their MBA experience. Students will share their backgrounds and how they have cultivated their personal and professional interests while at HBS.
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- Mar 2020
- Conference Presentation
A New Analysis of Differential Privacy's Generalization Guarantees
By: Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Moshe Shenfeld
We give a new proof of the "transfer theorem" underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially private and sample-accurate is also accurate out-of-sample. Our new proof is elementary and...
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Jung, Christopher, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Moshe Shenfeld. "A New Analysis of Differential Privacy's Generalization Guarantees." Paper presented at the 11th Innovations in Theoretical Computer Science Conference, Seattle, March 2020.
- 23 Oct 2020
- News
Now Is the Time to Shake Up Your Sales Processes
- 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...
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Krishna, Satyapriya, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, and Himabindu Lakkaraju. "The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective." Working Paper, 2022.
- 2022
- Article
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis.
By: Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay and Himabindu Lakkaraju
As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explanations in practice. Despite the growing popularity of counterfactual explanations, a...
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Keywords:
Machine Learning Models;
Counterfactual Explanations;
Adversarial Examples;
Mathematical Methods
Pawelczyk, Martin, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, and Himabindu Lakkaraju. "Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 25th (2022).
- April 2024
- Article
Detecting Routines: Applications to Ridesharing CRM
By: Ryan Dew, Eva Ascarza, Oded Netzer and Nachum Sicherman
Routines shape many aspects of day-to-day consumption. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines—which we define as repeated behaviors with recurring, temporal...
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Keywords:
Ride-sharing;
Routine;
Machine Learning;
Customer Relationship Management;
Consumer Behavior;
Segmentation
Dew, Ryan, Eva Ascarza, Oded Netzer, and Nachum Sicherman. "Detecting Routines: Applications to Ridesharing CRM." Journal of Marketing Research (JMR) 61, no. 2 (April 2024): 368–392.
- Article
Oracle Efficient Private Non-Convex Optimization
By: Seth Neel, Aaron Leon Roth, Giuseppe Vietri and Zhiwei Steven Wu
One of the most effective algorithms for differentially private learning and optimization is objective perturbation. This technique augments a given optimization problem (e.g. deriving from an ERM problem) with a random linear term, and then exactly solves it....
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Neel, Seth, Aaron Leon Roth, Giuseppe Vietri, and Zhiwei Steven Wu. "Oracle Efficient Private Non-Convex Optimization." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020).
- 2023
- Working Paper
Beefing IT Up for Your Investor? Open Sourcing and Startup Funding: Evidence from GitHub
By: Annamaria Conti, Christian Peukert and Maria Roche
We study the participation of nascent firms in open source communities and its implications for attracting funding. To do so, we link data on 160,065 U.S. startups from Crunchbase to their activities on the open source development platform GitHub. In a matched sample...
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Keywords:
Startups;
Technology Strategy;
GitHub;
Machine Learning;
Business Startups;
Venture Capital;
Information Technology;
Strategy
Conti, Annamaria, Christian Peukert, and Maria Roche. "Beefing IT Up for Your Investor? Open Sourcing and Startup Funding: Evidence from GitHub." Harvard Business School Working Paper, No. 22-001, July 2021. (Revised August 2023.)
- Research Summary
Overview
I develop machine learning tools and techniques which enable human decision makers to make better decisions. More specifically, my research addresses the following fundamental questions pertaining to human and algorithmic decision-making:
1. How to build... View Details
1. How to build... View Details
- 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...
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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.
- December 1, 2021
- Article
Do You Know How Your Teams Get Work Done?
By: Rohan Narayana Murty, Rajath B. Das, Scott Duke Kominers, Arjun Narayan, Suraj Srinivasan, Tarun Khanna and Kartik Hosanagar
In a research study at four Fortune 500 companies, when managers were asked about their teams’ work, on average they either did not know or could not remember 60% of the work their teams do. This is a major problem because it can lead to unrealistic digital...
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Keywords:
Leading Teams;
Work Recall Gap;
Machine Learning;
Algorithms;
Groups and Teams;
Management;
Technological Innovation
Murty, Rohan Narayana, Rajath B. Das, Scott Duke Kominers, Arjun Narayan, Suraj Srinivasan, Tarun Khanna, and Kartik Hosanagar. "Do You Know How Your Teams Get Work Done?" Harvard Business Review Digital Articles (December 1, 2021).
- 14 Aug 2017
- Conference Presentation
A Convex Framework for Fair Regression
By: Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel and Aaron Roth
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems. These regularizers all enjoy convexity, permitting fast optimization, and they span the range from notions of group fairness to strong individual fairness. By varying...
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Berk, Richard, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. "A Convex Framework for Fair Regression." Paper presented at the 4th Workshop on Fairness, Accountability, and Transparency in Machine Learning, Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), August 14, 2017.
- 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.)
- Article
Who, When, and Why: A Machine Learning Approach to Prioritizing Students at Risk of Not Graduating High School on Time
By: Everaldo Aguiar, Himabindu Lakkaraju, Nasir Bhanpuri, David Miller, Ben Yuhas, Kecia Addison and Rayid Ghani
Aguiar, Everaldo, Himabindu Lakkaraju, Nasir Bhanpuri, David Miller, Ben Yuhas, Kecia Addison, and Rayid Ghani. "Who, When, and Why: A Machine Learning Approach to Prioritizing Students at Risk of Not Graduating High School on Time." Proceedings of the International Learning Analytics and Knowledge Conference 5th (2015).
- October 2023
- Article
Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA
By: Matthew S. Johnson, David I. Levine and Michael W. Toffel
We study how a regulator can best target inspections. Our case study is a U.S. Occupational Safety and Health Administration (OSHA) program that randomly allocated some inspections. On average, each inspection averted 2.4 serious injuries (9%) over the next five years....
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Keywords:
Safety Regulations;
Regulations;
Regulatory Enforcement;
Machine Learning Models;
Safety;
Operations;
Service Operations;
Production;
Forecasting and Prediction;
Decisions;
United States
Johnson, Matthew S., David I. Levine, and Michael W. Toffel. "Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA." American Economic Journal: Applied Economics 15, no. 4 (October 2023): 30–67. (Profiled in the Regulatory Review.)