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- 2023
- Working Paper
Auditing Predictive Models for Intersectional Biases
By: Kate S. Boxer, Edward McFowland III and Daniel B. Neill
Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for individuals at the intersection of two or more protected classes. To address this risk, we... View Details
Boxer, Kate S., Edward McFowland III, and Daniel B. Neill. "Auditing Predictive Models for Intersectional Biases." Working Paper, June 2023.
- 2014
- Article
The Promise of Prediction Contests
By: Phillip E. Pfeifer, Yael Grushka-Cockayne and Kenneth C. Lichtendahl
This article examines the prediction contest as a vehicle for aggregating the opinions of a crowd of experts. After proposing a general definition distinguishing prediction contests from other mechanisms for harnessing the wisdom of crowds, we focus on... View Details
Pfeifer, Phillip E., Yael Grushka-Cockayne, and Kenneth C. Lichtendahl. "The Promise of Prediction Contests." American Statistician 68, no. 4 (2014): 264–270.
- February 2021
- Tutorial
Assessing Prediction Accuracy of Machine Learning Models
By: Michael Toffel and Natalie Epstein
This video describes how to assess the accuracy of machine learning prediction models, primarily in the context of machine learning models that predict binary outcomes, such as logistic regression, random forest, or nearest neighbor models. After introducing and... View Details
- 2022
- Working Paper
TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations
By: Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju and Sameer Singh
Practitioners increasingly use machine learning (ML) models, yet they have become more complex and harder to understand. To address this issue, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use explainability... View Details
Slack, Dylan, Satyapriya Krishna, Himabindu Lakkaraju, and Sameer Singh. "TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations." Working Paper, 2022.
- August 2020 (Revised September 2020)
- Technical Note
Assessing Prediction Accuracy of Machine Learning Models
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools... View Details
Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods
Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
- 9 Dec 2016
- Conference Presentation
Discovering Unknown Unknowns of Predictive Models
By: Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Eric Horvitz
Lakkaraju, Himabindu, Ece Kamar, Rich Caruana, and Eric Horvitz. "Discovering Unknown Unknowns of Predictive Models." Paper presented at the 30th Annual Conference on Neural Information Processing Systems (NIPS), Workshop on Reliable Machine Learning in the Wild, Barcelona, Spain, December 9, 2016.
- 2023
- Article
Provable Detection of Propagating Sampling Bias in Prediction Models
By: Pavan Ravishankar, Qingyu Mo, Edward McFowland III and Daniel B. Neill
With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider... View Details
Ravishankar, Pavan, Qingyu Mo, Edward McFowland III, and Daniel B. Neill. "Provable Detection of Propagating Sampling Bias in Prediction Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9562–9569. (Presented at the 37th AAAI Conference on Artificial Intelligence (2/7/23-2/14/23) in Washington, DC.)
- 2022
- Working Paper
Machine Learning Models for Prediction of Scope 3 Carbon Emissions
By: George Serafeim and Gladys Vélez Caicedo
For most organizations, the vast amount of carbon emissions occur in their supply chain and in the post-sale processing, usage, and end of life treatment of a product, collectively labelled scope 3 emissions. In this paper, we train machine learning algorithms on 15... View Details
Keywords: Carbon Emissions; Climate Change; Environment; Carbon Accounting; Machine Learning; Artificial Intelligence; Digital; Data Science; Environmental Sustainability; Environmental Management; Environmental Accounting
Serafeim, George, and Gladys Vélez Caicedo. "Machine Learning Models for Prediction of Scope 3 Carbon Emissions." Harvard Business School Working Paper, No. 22-080, June 2022.
- December 2005
- Article
Adjusting Choice Models to Better Predict Market Behavior
By: Greg Allenby, Geraldine Fennel, Joel Huber, Thomas Eagle, Tim Gilbride, Jaehwan Kim, Peter Lenk, Rich Johnson, Bryan Orme, Elie Ofek, Thomas Otter and Joan Walker
Allenby, Greg, Geraldine Fennel, Joel Huber, Thomas Eagle, Tim Gilbride, Jaehwan Kim, Peter Lenk, Rich Johnson, Bryan Orme, Elie Ofek, Thomas Otter, and Joan Walker. "Adjusting Choice Models to Better Predict Market Behavior." Marketing Letters 16, nos. 3/4 (December 2005).
- January 2021
- Article
Using Models to Persuade
By: Joshua Schwartzstein and Adi Sunderam
We present a framework where "model persuaders" influence receivers’ beliefs by proposing models that organize past data to make predictions. Receivers are assumed to find models more compelling when they better explain the data, fixing receivers’ prior beliefs. Model... View Details
Keywords: Model Persuasion; Analytics and Data Science; Forecasting and Prediction; Mathematical Methods; Framework
Schwartzstein, Joshua, and Adi Sunderam. "Using Models to Persuade." American Economic Review 111, no. 1 (January 2021): 276–323.
- July– September 2002
- Article
Predictive Value and the Usefulness of Game Theoretic Models
By: Ido Erev, Alvin E. Roth, Robert L. Slonim and Greg Barron
Erev, Ido, Alvin E. Roth, Robert L. Slonim, and Greg Barron. "Predictive Value and the Usefulness of Game Theoretic Models." International Journal of Forecasting 18, no. 3 (July– September 2002): 359–368.
- 2023
- Working Paper
Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development
By: Daniel Yue, Paul Hamilton and Iavor Bojinov
Predictive model development is understudied despite its centrality in modern artificial
intelligence and machine learning business applications. Although prior discussions
highlight advances in methods (along the dimensions of data, computing power, and
algorithms)... View Details
Keywords: Analytics and Data Science
Yue, Daniel, Paul Hamilton, and Iavor Bojinov. "Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development." Harvard Business School Working Paper, No. 23-029, December 2022. (Revised April 2023.)
- January 2021
- Article
A Model of Relative Thinking
By: Benjamin Bushong, Matthew Rabin and Joshua Schwartzstein
Fixed differences loom smaller when compared to large differences. We propose a model of relative thinking where a person weighs a given change along a consumption dimension by less when it is compared to bigger changes along that dimension. In deterministic settings,... View Details
Bushong, Benjamin, Matthew Rabin, and Joshua Schwartzstein. "A Model of Relative Thinking." Review of Economic Studies 88, no. 1 (January 2021): 162–191.
- 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... View Details
Rawal, Kaivalya, and Himabindu Lakkaraju. "Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses." Advances in Neural Information Processing Systems (NeurIPS) 33 (2020).
- Article
Active World Model Learning with Progress Curiosity
By: Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber and Daniel Yamins
World models are self-supervised predictive models of how the world evolves. Humans learn world models by curiously exploring their environment, in the process acquiring compact abstractions of high bandwidth sensory inputs, the ability to plan across long temporal... View Details
Kim, Kuno, Megumi Sano, Julian De Freitas, Nick Haber, and Daniel Yamins. "Active World Model Learning with Progress Curiosity." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020).
- Article
Learning Models for Actionable Recourse
By: Alexis Ross, Himabindu Lakkaraju and Osbert Bastani
As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for generating counterfactual explanations. Such explanations provide individuals adversely... View Details
Ross, Alexis, Himabindu Lakkaraju, and Osbert Bastani. "Learning Models for Actionable Recourse." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- May 2006
- Article
Detection Defection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models
By: Scott Neslin, Sunil Gupta, Wagner Kamakura, Junxiang Lu and Charlotte Mason
Neslin, Scott, Sunil Gupta, Wagner Kamakura, Junxiang Lu, and Charlotte Mason. "Detection Defection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models." Journal of Marketing Research (JMR) 43, no. 2 (May 2006): 204–211.
- 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... View Details
Lakkaraju, Himabindu, Ece Kamar, Rich Caruana, and Jure Leskovec. "Faithful and Customizable Explanations of Black Box Models." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2019).
- 2024
- Working Paper
Scaling Core Earnings Measurement with Large Language Models
By: Matthew Shaffer and Charles CY Wang
We study the application of large language models (LLMs) to the estimation of core earnings, i.e., a firm's persistent profitability from its core business activities. This construct is central to investors' assessments of economic performance and valuations. However,... View Details
Keywords: Large Language Models; AI and Machine Learning; Accounting; Profit; Corporate Disclosure; Analytics and Data Science; Measurement and Metrics
Shaffer, Matthew, and Charles CY Wang. "Scaling Core Earnings Measurement with Large Language Models." Working Paper, November 2024.
- 1995
- Chapter
Alternative Models of Negotiated Outcomes and the Nontraditional Utility Concerns That Limit Their Predictability
By: S. B. White, M. H. Bazerman and M. A. Neale
White, S. B., M. H. Bazerman, and M. A. Neale. "Alternative Models of Negotiated Outcomes and the Nontraditional Utility Concerns That Limit Their Predictability." In Research on Negotiation in Organizations, edited by R. J. Bies, R. Lewicki, and B. Sheppard. Greenwich, CT: JAI Press, 1995.