Filter Results
:
(98)
Show Results For
- All HBS Web (98)
- Faculty Publications (36)
Show Results For
- All HBS Web (98)
- Faculty Publications (36)
- November, 2016
- Article
Fixing Discrimination in Online Marketplaces
By: Ray Fisman and Michael Luca
Online marketplaces such as eBay, Uber, and Airbnb have the potential to reduce racial, gender, and other forms of bias that affect the off-line world. And in the early days of Internet commerce, the relative anonymity of transactions did make it harder for...
View Details
Fisman, Ray, and Michael Luca. "Fixing Discrimination in Online Marketplaces." Harvard Business Review 94, no. 12 (November, 2016): 88–95.
- 28 Feb 2018
- HBS Seminar
Kartik Hosanagar, Wharton, University of Pennsylvania
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
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,...
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
Keywords:
Machine Learning;
Econometric Analysis;
Instrumental Variable;
Random Forest;
Causal Inference;
AI and Machine Learning;
Forecasting and Prediction
Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
- 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
Menghani, Neil, Edward McFowland III, and Daniel B. Neill. "Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness." Working Paper, June 2023.
- 19 Feb 2019
- First Look
New Research and Ideas, February 19, 2019
algorithm uncovers two distinct behavioral types: "leaders" and "managers." Leaders focus on multi-function, high-level meetings, while managers focus on one-to-one meetings with core functions. Firms with leader CEOs are on average more...
View Details
Keywords:
Sean Silverthorne
- May 2022 (Revised April 2023)
- 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 April 2023.)
- 27 Feb 2018
- First Look
First Look at New Research and Ideas, February 27, 2018
entrepreneurs are known to raise higher levels of funding than their female counterparts, but the underlying mechanism for this funding disparity remains contested. Drawing upon Regulatory Focus Theory, we propose that the gap originates with a gender View Details
Keywords:
Sean Silverthorne
- 19 Jan 2023
- Research & Ideas
What Makes Employees Trust (vs. Second-Guess) AI?
When an algorithm recommends ways to improve business outcomes, do employees trust it? Conventional wisdom suggests that understanding the inner workings of artificial intelligence (AI) can raise confidence in such programs. Yet, new...
View Details
Keywords:
by Rachel Layne
- Web
Students on the Job Market - Doctoral
access to an algorithm’s recommendations, we confirm this bias towards NAW and find that it leads to a 20-61% increase in prediction error. In a follow-up experiment, we find that feature transparency – even when the underlying View Details
- 03 May 2023
- Research & Ideas
Why Confronting Racism in AI 'Creates a Better Future for All of Us'
share research on the intersection of race and capitalism—specifically, on race and technology since we live in the age of the digital transition and race is inherently embedded in algorithms and code simply because of who created the...
View Details
Keywords:
by Barbara DeLollis
- Web
Racial Equity Fellows - Institute for the Study of Business in Global Society
the racial bias in algorithms used by social media platforms, which may have coded rules that alter which faces are seen by users. Chyei Vinluan Postdoctoral Fellow of Business Administration, Harvard...
View Details
- 01 Mar 2023
- News
Is AI OK?
Illustrations by Dan Bejar Illustrations by Dan Bejar When she began investigating Airbnb’s smart-pricing algorithm several years ago, Assistant Professor Shunyuan Zhang wasn’t looking for evidence that it generated discriminatory outcomes, a problem known as...
View Details
- 08 Apr 2014
- First Look
First Look: April 8
Lee, Charles M.C., Paul Ma, and Charles C.Y. Wang Abstract—Applying a "co-search" algorithm to Internet traffic at the SEC's EDGAR website, we develop a novel method for identifying economically related peer firms. Our results...
View Details
Keywords:
Sean Silverthorne
- 07 Feb 2022
- Research & Ideas
Digital Transformation: A New Roadmap for Success
algorithms can lead to unintended bias that harms certain employees and customers, and the company’s reputation (a bias story can go viral on social media within minutes). 5....
View Details
- 10 Feb 2020
- In Practice
6 Ways That Emerging Technology Is Disrupting Business Strategy
Economic Research. 3. Algorithms are changing the pricing game “Firms are increasingly using pricing algorithms to set prices, especially in online markets. Pricing View Details
Keywords:
by Danielle Kost
- 01 Dec 2023
- News
Thinking Ahead
As we wind down 2023, there’s talk everywhere of generative AI and how it will fundamentally alter the world as we know it; but how does that translate for your corner of the business world? Is TikTok something you need to take seriously? (Is it time to dance?) We...
View Details
- 12 Apr 2022
- Research & Ideas
Swiping Right: How Data Helped This Online Dating Site Make More Matches
we had no real idea what would prevail. So, this is where the scientific mystery lies,” he says. Applying dating algorithms to other industries, cautiously Platonic platforms could follow similar, industry-appropriate revelation models....
View Details
Keywords:
by Kara Baskin
- 28 Jul 2020
- Research & Ideas
Racism and Digital Design: How Online Platforms Can Thwart Discrimination
inclusive design choices in a forthcoming article in the journal Marketing Intelligence Review. What follows is a condensed version: Build awareness. Digital platform builders must recognize how their design choices and algorithms can...
View Details
- Web
2023 Reunion Presentations - Alumni
STUDY: The Dark Side of AI: Algorithmic Bias and Discrimination Associate Professor Ayelet Israeli + More Info – Less Info This modified case discussion does not require pre-reading.Digitalization and...
View Details