Publications
Publications
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- All HBS Web
(1,107)
- Faculty Publications (273)
- 2023
- Article
Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
By: Suraj Srinivas, Sebastian Bordt and Himabindu Lakkaraju
One of the remarkable properties of robust computer vision models is that their input-gradients are often aligned with human perception, referred to in the literature as perceptually-aligned gradients (PAGs). Despite only being trained for classification, PAGs cause... View Details
- September 2023
- Article
A Pull versus Push Framework for Reputation
Reputation is a powerful driver of human behavior. Reputation systems incentivize 'actors' to take reputation-enhancing actions, and 'evaluators' to reward actors with positive reputations by preferentially cooperating with them. This article proposes a reputation... 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
- July 2023
- Article
Design and Analysis of Switchback Experiments
By: Iavor I Bojinov, David Simchi-Levi and Jinglong Zhao
In switchback experiments, a firm sequentially exposes an experimental unit to a random treatment, measures its response, and repeats the procedure for several periods to determine which treatment leads to the best outcome. Although practitioners have widely adopted... View Details
- June 2023
- Article
National Customer Orientation: An Empirical Test across 112 Countries
By: Ofer Mintz, Imran S. Currim and Rohit Deshpandé
Customer orientation is a central tenet of marketing. However, less is known about how customer orientation varies across countries and time. Mintz, Currim, and Deshpandé (Eur. J. Mark., 56: 1014–1041, 2022) propose a country-level construct, national customer... View Details
- April 2023
- Article
The Subjective Expected Utility Approach and a Framework for Defining Project Risk in Terms of Novelty and Feasibility—A Response to Franzoni and Stephan (2023), ‘Uncertainty and Risk-Taking in Science’
In their Discussion Paper, Franzoni and Stephan (F&S, 2023) discuss the shortcomings of existing peer review models in shaping the funding of risky science. Their discussion offers a conceptual framework for incorporating risk into peer review models of research... View Details
- 2023
- Working Paper
Distributionally Robust Causal Inference with Observational Data
By: Dimitris Bertsimas, Kosuke Imai and Michael Lingzhi Li
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders. Our approach is based on distributionally robust optimization and proceeds in two steps. We first... View Details
- February 2023
- Article
The Effect of Systems of Management Controls on Honesty in Managerial Reporting
By: Aishwarrya Deore, Susanna Gallani and Ranjani Krishnan
While budgetary controls with capital rationing are optimal in theory and widespread in practice, empirical research documents their association with higher employee dishonesty compared to budgetary controls without rationing. In this study, we examine whether... View Details
- 2023
- Working Paper
The Subjective Expected Utility Approach and a Framework for Defining Project Risk in Terms of Novelty and Feasibility—A Response to Franzoni and Stephan (2023), ‘Uncertainty and Risk-Taking in Science’
In their Discussion Paper, Franzoni and Stephan (F&S, 2023) discuss the shortcomings of existing peer review models in shaping the funding of risky science. Their discussion offers a conceptual framework for incorporating risk into peer review models of research... View Details
- January 2023
- Article
Psychological Safety Comes of Age: Observed Themes in an Established Literature
By: Amy C. Edmondson and Derrick P. Bransby
Since its renaissance in the 1990s, psychological safety research has
flourished—a boom motivated by recognition of the challenge of navigating uncertainty and change. Today, its theoretical and practical significance
is amplified by the increasingly complex and... View Details
- 2022
- Article
Becoming a Learning Organization While Enhancing Performance: The Case of LEGO
By: Thomas Borup Kristensen, Henrik Saabye and Amy Edmondson
Purpose - The purpose of this study is to empirically test how problem-solving lean practices, along with
leaders as learning facilitators in an action learning approach, can be transferred from a production context to a
knowledge work context for the purpose... View Details
- 2022
- Article
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations
By: Tessa Han, Suraj Srinivas and Himabindu Lakkaraju
A critical problem in the field of post hoc explainability is the lack of a common foundational goal among methods. For example, some methods are motivated by function approximation, some by game theoretic notions, and some by obtaining clean visualizations. This... View Details
- October–December 2022
- Article
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed... View Details
- 2022
- Article
The Turn Toward Creative Work
By: Spencer Harrison, Elizabeth D. Rouse, Colin M. Fisher and Teresa M. Amabile
In this Academy of Management Collections essay, we curate a set of articles from the Academy of Management family of journals that showcase the evolution of creativity research within organizational scholarship. The articles reveal a shift from the study of... View Details
- 2022
- Article
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations
By: Jessica Dai, Sohini Upadhyay, Ulrich Aivodji, Stephen Bach and Himabindu Lakkaraju
As post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to ensure that the quality of the resulting explanations is consistently high across all subgroups of a population. For instance, it... View Details
- 2022
- Book
Empires of Ideas: Creating the Modern University from Germany to America to China
By: William C. Kirby
The modern university was born in Germany. In the twentieth century, the United States leapfrogged Germany to become the global leader in higher education. Will China challenge its position in the twenty-first?
Today American institutions dominate nearly every... View Details
Today American institutions dominate nearly every... View Details
- July 2022
- Article
Estimating Spillovers from Publicly Funded R&D: Evidence from the US Department of Energy
By: Kyle Myers and Lauren Lanahan
We quantify the magnitude of R&D spillovers created by grants to small firms from the US Department of Energy. Our empirical strategy leverages variation due to state-specific matching policies, and we develop a new approach to measuring both geographic and... View Details
Keywords: Innovation; Energy; R＆D; Grants; Innovation and Invention; Research and Development; Patents; Performance; United States
- 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... View Details
- 2022
- Article
Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods.
By: Chirag Agarwal, Marinka Zitnik and Himabindu Lakkaraju
As Graph Neural Networks (GNNs) are increasingly employed in real-world applications, it becomes critical to ensure that the stakeholders understand the rationale behind their predictions. While several GNN explanation methods have been proposed recently, there has... View Details