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(653)
- News (145)
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- Faculty Publications (300)
Show Results For
- All HBS Web
(653)
- News (145)
- Research (425)
- Events (20)
- Multimedia (12)
- Faculty Publications (300)
- 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
Scharfstein, David S., and Sergey Chernenko. "The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities." Working Paper, April 2023.
- 09 Mar 2020
- News
Warring Algorithms Could Be Driving Up Consumer Prices
- 19 May 2022
- News
Algorithmic Pricing Is Both Efficient and Absurd
The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities
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... View Details
- 20 Dec 2018
- News
Consumer Rating Algorithms Score Big with Businesses, Governments
- 20 Dec 2017
- News
Even Imperfect Algorithms Can Improve the Criminal Justice System
- 2024
- Article
A Universal In-Place Reconfiguration Algorithm for Sliding Cube-Shaped Robots in Quadratic Time
By: Zachary Abel, Hugo A. Akitaya, Scott Duke Kominers, Matias Korman and Frederick Stock
In the modular robot reconfiguration problem we are given n cube-shaped modules (or "robots") as well as two configurations, i.e., placements of the n modules so that their union is face-connected. The goal is to find a sequence of moves that reconfigures the modules... View Details
Abel, Zachary, Hugo A. Akitaya, Scott Duke Kominers, Matias Korman, and Frederick Stock. "A Universal In-Place Reconfiguration Algorithm for Sliding Cube-Shaped Robots in Quadratic Time." Proceedings of the International Symposium on Computational Geometry (SoCG) 40th (2024): 1:1–1:14.
How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness
What is the best way to define algorithmic fairness? While many definitions of fairness have been proposed in the computer science literature, there is no clear agreement over a particular definition. In this work, we investigate ordinary people’s perceptions of three... View Details
- Article
Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs
By: Michael G. Endres, Florian Hillen, Marios Salloumis, Ahmad R. Sedaghat, Stefan M. Niehues, Olivia Quatela, Henning Hanken, Ralf Smeets, Benedicta Beck-Broichsitter, Carsten Rendenbach, Karim R. Lakhani, Max Helland and Robert A. Gaudin
Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts, and tumors. In this study, we seek to investigate the... View Details
Keywords: Artificial Intelligence; Diagnosis; Computer-assisted; Image Interpretation; Machine Learning; Radiography; Panoramic Radiograph; AI and Machine Learning
Endres, Michael G., Florian Hillen, Marios Salloumis, Ahmad R. Sedaghat, Stefan M. Niehues, Olivia Quatela, Henning Hanken, Ralf Smeets, Benedicta Beck-Broichsitter, Carsten Rendenbach, Karim R. Lakhani, Max Helland, and Robert A. Gaudin. "Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs." Diagnostics 10, no. 6 (June 2020).
- 29 Sep 2023
- News
Eliminating Algorithmic Bias Is Just the Beginning of Equitable AI
- 28 Feb 2018
- News
Case Study: Should an Algorithm Tell You Who to Promote?
- 2021
- Article
Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring
By: Tom Sühr, Sophie Hilgard and Himabindu Lakkaraju
Ranking algorithms are being widely employed in various online hiring platforms including LinkedIn, TaskRabbit, and Fiverr. Prior research has demonstrated that ranking algorithms employed by these platforms are prone to a variety of undesirable biases, leading to the... View Details
Sühr, Tom, Sophie Hilgard, and Himabindu Lakkaraju. "Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society 4th (2021).
- Article
How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness
By: Nripsuta Saxena, Karen Huang, Evan DeFilippis, Goran Radanovic, David C. Parkes and Yang Liu
What is the best way to define algorithmic fairness? While many definitions of fairness have been proposed in the computer science literature, there is no clear agreement over a particular definition. In this work, we investigate ordinary people’s perceptions of three... View Details
Saxena, Nripsuta, Karen Huang, Evan DeFilippis, Goran Radanovic, David C. Parkes, and Yang Liu. "How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2019).
- Article
The Effects of the Change in the NRMP Matching Algorithm
By: A. E. Roth and Elliott Peranson
Roth, A. E., and Elliott Peranson. "The Effects of the Change in the NRMP Matching Algorithm." JAMA, the Journal of the American Medical Association 278, no. 9 (September 3, 1997): 729–732.
- 04 Jan 2023
- News
Beyond Bias: Improving Workplace Diversity in the Age of Algorithms
- Article
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
By: Seth Neel and Aaron Leon Roth
Data that is gathered adaptively—via bandit algorithms, for example—exhibits bias. This is true both when gathering simple numeric valued data—the empirical means kept track of by stochastic bandit algorithms are biased downwards—and when gathering more complicated... View Details
Neel, Seth, and Aaron Leon Roth. "Mitigating Bias in Adaptive Data Gathering via Differential Privacy." Proceedings of the International Conference on Machine Learning (ICML) 35th (2018).
- Article
The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables
By: Himabindu Lakkaraju, Jon Kleinberg, Jure Leskovec, Jens Ludwig and Sendhil Mullainathan
Lakkaraju, Himabindu, Jon Kleinberg, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan. "The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables." Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining 23rd (2017).
- 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
Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. "Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse." Proceedings of the International Conference on Learning Representations (ICLR) (2023).
- 07 May 2020
- News