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- All HBS Web
(4,157)
- Faculty Publications (1,261)
- May 2022
- Case
AWS and Amazon SageMaker (A): The Commercialization of Machine Learning Services
By: Karim R. Lakhani, Shane Greenstein and Kerry Herman
Lakhani, Karim R., Shane Greenstein, and Kerry Herman. "AWS and Amazon SageMaker (A): The Commercialization of Machine Learning Services." Harvard Business School Case 622-060, May 2022.
- May 2022
- Supplement
AWS and Amazon SageMaker (B): The Commercialization of Machine Learning Services
By: Karim R. Lakhani, Shane Greenstein and Kerry Herman
Lakhani, Karim R., Shane Greenstein, and Kerry Herman. "AWS and Amazon SageMaker (B): The Commercialization of Machine Learning Services." Harvard Business School Supplement 622-086, May 2022.
- May 2022 (Revised July 2022)
- Supplement
AWS and Amazon SageMaker (C): The Commercialization of Machine Learning Services
By: Karim R. Lakhani, Shane Greenstein and Kerry Herman
Lakhani, Karim R., Shane Greenstein, and Kerry Herman. "AWS and Amazon SageMaker (C): The Commercialization of Machine Learning Services." Harvard Business School Supplement 622-087, May 2022. (Revised July 2022.)
- May 2022 (Revised June 2024)
- Case
LOOP: Driving Change in Auto Insurance Pricing
By: Elie Ofek and Alicia Dadlani
John Henry and Carey Anne Nadeau, co-founders and co-CEOs of LOOP, an insurtech startup based in Austin, Texas, were on a mission to modernize the archaic $250 billion automobile insurance market. They sought to create equitably priced insurance by eliminating pricing... View Details
Keywords: AI and Machine Learning; Technological Innovation; Equality and Inequality; Prejudice and Bias; Growth and Development Strategy; Customer Relationship Management; Price; Insurance Industry; Financial Services Industry
Ofek, Elie, and Alicia Dadlani. "LOOP: Driving Change in Auto Insurance Pricing." Harvard Business School Case 522-073, May 2022. (Revised June 2024.)
- May 5, 2022
- Article
How to Build a Life: Ben Franklin’s Radical Theory of Happiness
By: Arthur C. Brooks
Brooks, Arthur C. "How to Build a Life: Ben Franklin’s Radical Theory of Happiness." The Atlantic (May 5, 2022).
- May 2022 (Revised July 2022)
- Case
The Voice War Continues: Hey Google vs. Alexa vs. Siri in 2022
By: David B. Yoffie and Daniel Fisher
In 2022, after five years of pursuing a new "AI-first" strategy, Google had captured a sizeable share of the American and global markets for voice assistants. Google Assistant was used by hundreds of millions of users around the world, but Amazon retained the largest... View Details
Keywords: Strategy; Artificial Intelligence; Deep Learning; Voice Assistants; Smart Home; Market Share; Globalized Markets and Industries; Competitive Strategy; Digital Platforms; AI and Machine Learning; Technology Industry; United States
Yoffie, David B., and Daniel Fisher. "The Voice War Continues: Hey Google vs. Alexa vs. Siri in 2022." Harvard Business School Case 722-462, May 2022. (Revised July 2022.)
- May 2022
- Article
Coins for Bombs: The Predictive Ability of On-Chain Transfers for Terrorist Attacks
By: Dan Amiram, Evgeny Lyandres and Daniel Rabetti
This study examines whether we can learn from the behavior of blockchain-based transfers to predict the financing of terrorist attacks. We exploit blockchain transaction transparency to map millions of transfers for hundreds of large on-chain service providers. The... View Details
Keywords: Blockchain; Bitcoin; Accounting; AI and Machine Learning; National Security; Governing Rules, Regulations, and Reforms
Amiram, Dan, Evgeny Lyandres, and Daniel Rabetti. "Coins for Bombs: The Predictive Ability of On-Chain Transfers for Terrorist Attacks." Journal of Accounting Research 60, no. 2 (May 2022): 427–466.
- Article
Developing a Digital Mindset: How to Lead Your Organization into the Age of Data, Algorithms, and AI
By: Tsedal Neeley and Paul Leonardi
Learning new technological skills is essential for digital transformation. But it is not enough. Employees must be motivated to use their skills to create new opportunities. They need a digital mindset: a set of attitudes and behaviors that enable people and... View Details
Keywords: Machine Learning; AI; Information Technology; Transformation; Competency and Skills; Employees; Technology Adoption; Leading Change; Digital Transformation
Neeley, Tsedal, and Paul Leonardi. "Developing a Digital Mindset: How to Lead Your Organization into the Age of Data, Algorithms, and AI." S22032. Harvard Business Review 100, no. 3 (May–June 2022): 50–55.
- 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
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).
- 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
Keywords: Graph Neural Networks; Explanation Methods; Mathematical Methods; Framework; Theory; Analysis
Agarwal, Chirag, Marinka Zitnik, and Himabindu Lakkaraju. "Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 25th (2022).
- 2022
- Book
The Digital Mindset: What It Really Takes to Thrive in the Age of Data, Algorithms, and AI
By: Paul Leonardi and Tsedal Neeley
The pressure to "be digital" has never been greater, but you can meet the challenge.
The digital revolution is here, changing how work gets done, how industries are structured, and how people from all walks of life work, behave, and relate to each other. To thrive... View Details
Keywords: Digital; Artificial Intelligence; Big Data; Digital Transformation; Technological Innovation; Transformation; Learning; Competency and Skills
Leonardi, Paul, and Tsedal Neeley. The Digital Mindset: What It Really Takes to Thrive in the Age of Data, Algorithms, and AI. Boston, MA: Harvard Business Review Press, 2022.
- April 27, 2022
- Article
Inequality in Researchers' Minds: Four Guiding Questions for Studying Subjective Perceptions of Economic Inequality
By: Jon M. Jachimowicz, Shai Davidai, Daniela Goya-Tocchetto, Barnabas Szaszi, Martin Day, Stephanie Tepper, L. Taylor Phillips, M. Usman Mirza, Nailya Ordabayeva and Oliver P. Hauser
Subjective perceptions of inequality can substantially influence policy attitudes, public health metrics, and societal well-being, but the lack of consensus in the scientific community on how to best operationalize and measure these perceptions may impede progress on... View Details
Jachimowicz, Jon M., Shai Davidai, Daniela Goya-Tocchetto, Barnabas Szaszi, Martin Day, Stephanie Tepper, L. Taylor Phillips, M. Usman Mirza, Nailya Ordabayeva, and Oliver P. Hauser. "Inequality in Researchers' Minds: Four Guiding Questions for Studying Subjective Perceptions of Economic Inequality." Journal of Economic Surveys (April 27, 2022).
- April–June 2022
- Other Article
Commentary on 'Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters'
There has been a substantial discussion in various methodological and applied literatures around causal inference; especially in the use of machine learning and statistical models to understand heterogeneity in treatment effects and to make optimal decision... View Details
Keywords: Causal Inference; Treatment Effect Estimation; Treatment Assignment Policy; Human-in-the-loop; Decision Making; Fairness
McFowland III, Edward. "Commentary on 'Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters'." INFORMS Journal on Data Science 1, no. 1 (April–June 2022): 21–22.
- 2022
- Chapter
Prioritarianism and Optimal Taxation
By: Matti Tuomala and Matthew Weinzierl
Prioritarianism has been at the center of the formal approach to optimal tax theory since its modern starting point in Mirrlees (1971), but most theorists’ use of it is motivated by tractability rather than explicit normative reasoning. We characterize analytically and... View Details
Keywords: Prioritarianism; Optimal Taxation; Utilitarianism; Redistribution; Inverse-optimum; Taxation; Theory; Policy
Tuomala, Matti, and Matthew Weinzierl. "Prioritarianism and Optimal Taxation." In Prioritarianism in Practice, edited by Matthew Adler and Ole Norheim. Cambridge University Press, 2022. (Also published in HBR Insights, December 2020.)
- March 2022 (Revised January 2025)
- Technical Note
Prediction & Machine Learning
By: Iavor I. Bojinov, Michael Parzen and Paul Hamilton
This note provides an introduction to machine learning for an introductory data science course. The note begins with a description of supervised, unsupervised, and reinforcement learning. Then, the note provides a brief explanation of the difference between traditional... View Details
Keywords: Machine Learning; Data Science; Learning; Analytics and Data Science; Performance Evaluation; AI and Machine Learning
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Prediction & Machine Learning." Harvard Business School Technical Note 622-101, March 2022. (Revised January 2025.)
- Article
Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)
By: Eva Ascarza and Ayelet Israeli
An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”... View Details
Keywords: Algorithm Bias; Personalization; Targeting; Generalized Random Forests (GRF); Discrimination; Customization and Personalization; Decision Making; Fairness; Mathematical Methods
Ascarza, Eva, and Ayelet Israeli. "Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)." e2115126119. Proceedings of the National Academy of Sciences 119, no. 11 (March 8, 2022).
- March 2022
- Article
Contractual Restrictions and Debt Traps
By: Ernest Liu and Benjamin N. Roth
Microcredit and other forms of small-scale finance have failed to catalyze entrepreneurship in developing countries. In these credit markets, borrowers and lenders often bargain over not only the interest rate but also implicit restrictions on types of investment. We... View Details
Liu, Ernest, and Benjamin N. Roth. "Contractual Restrictions and Debt Traps." Review of Financial Studies 35, no. 3 (March 2022): 1141–1182.
- March 2022
- Article
Loan Types and the Bank Lending Channel
By: Victoria Ivashina, Luc Laeven and Enrique Moral-Benito
Using credit-registry data for Spain and Peru, we document that four main types of commercial credit—asset-based loans, cash flow loans, trade finance and leasing—are easily identifiable and represent the bulk of corporate credit. We show that credit growth dynamics... View Details
Keywords: Bank Credit; Loan Types; Bank Lending Channel; Credit Registry; Banks and Banking; Credit; Financing and Loans
Ivashina, Victoria, Luc Laeven, and Enrique Moral-Benito. "Loan Types and the Bank Lending Channel." Journal of Monetary Economics 126 (March 2022): 171–187.
- March 2022
- Article
Winner Takes All? Tech Clusters, Population Centers, and the Spatial Transformation of U.S. Invention
By: Brad Chattergoon and William R. Kerr
U.S. invention has become increasingly concentrated around major tech centers since the 1970s, with implications for how much cities across the country share in concomitant local benefits. Is invention becoming a winner-takes-all race? We explore the rising spatial... View Details
Keywords: Clusters; Invention; Agglomeration; Artificial Intelligence; Innovation and Invention; Patents; Applications and Software; Industry Clusters; AI and Machine Learning
Chattergoon, Brad, and William R. Kerr. "Winner Takes All? Tech Clusters, Population Centers, and the Spatial Transformation of U.S. Invention." Art. 104418. Research Policy 51, no. 2 (March 2022).
- 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
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.