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2023
Working Paper

In-Context Unlearning: Language Models as Few Shot Unlearners

By: Martin Pawelczyk, Seth Neel and Himabindu Lakkaraju

Machine unlearning, the study of efficiently removing the impact of specific training points on the trained model, has garnered increased attention of late, driven by the need to comply with privacy regulations like the Right to be Forgotten. Although unlearning is... View Details

Keywords: AI and Machine Learning; Copyright; Information

2025
Working Paper

The Impact of Input Inaccuracy on Leveraging AI Tools: Evidence from Algorithmic Labor Scheduling

By: Caleb Kwon, Antonio Moreno and Ananth Raman

Problem Definition: Considerable academic and practitioner attention is placed on the value of ex-post interactions (i.e., overrides) in the human-AI interface. In contrast, relatively little attention has been paid to ex-ante human-AI interactions (e.g., the... View Details

Keywords: AI and Machine Learning; Employees; Performance Effectiveness

September 29, 2023
Article

Eliminating Algorithmic Bias Is Just the Beginning of Equitable AI

By: Simon Friis and James Riley

When it comes to artificial intelligence and inequality, algorithmic bias rightly receives a lot of attention. But it’s just one way that AI can lead to inequitable outcomes. To truly create equitable AI, we need to consider three forces through which it might make... View Details

Keywords: AI and Machine Learning; Prejudice and Bias; Equality and Inequality

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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

Keywords: Forecasting and Prediction; AI and Machine Learning; Outcome or Result

2023
Article

Towards Bridging the Gaps between the Right to Explanation and the Right to Be Forgotten

By: Himabindu Lakkaraju, Satyapriya Krishna and Jiaqi Ma

The Right to Explanation and the Right to be Forgotten are two important principles outlined to regulate algorithmic decision making and data usage in real-world applications. While the right to explanation allows individuals to request an actionable explanation for an... View Details

Keywords: Analytics and Data Science; AI and Machine Learning; Decision Making; Governing Rules, Regulations, and Reforms

July 2023
Case

DayTwo: Going to Market with Gut Microbiome (Abridged)

By: Ayelet Israeli

DayTwo is a young Israeli startup that applies research on the gut microbiome and machine learning algorithms to deliver personalized nutritional recommendations to its users in order to minimize blood sugar spikes after meals. After a first year of trial rollout in... View Details

Keywords: Business Startups; AI and Machine Learning; Nutrition; Market Entry and Exit; Product Marketing; Distribution Channels

2023
Working Paper

Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness

By: Neil Menghani, Edward McFowland III and Daniel B. Neill

In this paper, we develop a new criterion, "insufficiently justified disparate impact" (IJDI), for assessing whether recommendations (binarized predictions) made by an algorithmic decision support tool are fair. Our novel, utility-based IJDI criterion evaluates false... View Details

Keywords: AI and Machine Learning; Forecasting and Prediction; Prejudice and Bias

2023
Article

Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse

By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju

As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely impacted (e.g., loan denied) by the predictions of these models are provided with a means... View Details

Keywords: AI and Machine Learning; Decision Choices and Conditions; Mathematical Methods

April 2023
Article

On the Privacy Risks of Algorithmic Recourse

By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel

As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected... View Details

Keywords: Recourse; Privacy Threats; AI and Machine Learning; Information

2023
Working Paper

The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities

By: David S. Scharfstein and Sergey Chernenko

We show that the use of algorithms to predict race has significant limitations in measuring and understanding the sources of racial disparities in finance, economics, and other contexts. First, we derive theoretically the direction and magnitude of measurement bias in... View Details

Keywords: Racial Disparity; Paycheck Protection Program; Measurement Error; AI and Machine Learning; Race; Measurement and Metrics; Equality and Inequality; Prejudice and Bias; Forecasting and Prediction; Outcome or Result

March 2023
Teaching Note

VideaHealth: Building the AI Factory

By: Karim R. Lakhani

Teaching Note for HBS Case No. 621-021. The case “VideaHealth: Building the AI Factory” examines the creation of dental startup VideaHealth (Videa) and the development of its artificial intelligence (AI)-led business strategy through the eyes of founder and CEO Florian... View Details

Keywords: AI and Machine Learning; Applications and Software; Business Model; Marketing Strategy; Product Development; Health Industry; Technology Industry

2023
Chapter

Marketing Through the Machine’s Eyes: Image Analytics and Interpretability

By: Shunyuan Zhang, Flora Feng and Kannan Srinivasan

he growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility—if only the... View Details

Keywords: Transparency; Marketing Research; Algorithmic Bias; AI and Machine Learning; Marketing

2023
Working Paper

When Algorithms Explain Themselves: AI Adoption and Accuracy of Experts' Decisions

By: Himabindu Lakkaraju and Chiara Farronato

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

Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; AI and Machine Learning; Forecasting and Prediction

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September 2022 (Revised November 2022)
Teaching Note

PittaRosso: Artificial Intelligence-Driven Pricing and Promotion

By: Ayelet Israeli

Teaching Note for HBS Case No. 522-046. View Details

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

Article

Adaptive Machine Unlearning

By: Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Chris Waites

Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for sequences of deletions, most prior work in the non-convex setting gives valid guarantees... View Details

Keywords: Machine Learning; AI and Machine Learning

November 2021 (Revised December 2021)
Supplement

PittaRosso (B): Human and Machine Learning

By: Ayelet Israeli

This case supplements the "PittaRosso: Artificial Intelligence-Driven Pricing and Promotion" case, and provides major highlights on what happened at the company since the first case. View Details

October 2021 (Revised March 2022)
Supplement

PittaRosso: Artificial Intelligence-Driven Pricing and Promotion

By: Ayelet Israeli and Fabrizio Fantini

PittaRosso, a traditional Italian shoe retailer, is implementing an AI system to provide pricing and promotion recommendations. The system allows them to implement changes that would affect both the top of funnel and bottom of funnel activities for the company: once... View Details

October 2021 (Revised June 2022)
Case

PittaRosso: Artificial Intelligence-Driven Pricing and Promotion

By: Ayelet Israeli

PittaRosso, a traditional Italian shoe retailer, is implementing an AI system to provide pricing and promotion recommendations. The system allows them to implement changes that would affect both the top of funnel and bottom of funnel activities for the company: once... View Details

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