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(248)
- News (31)
- Research (166)
- Multimedia (3)
- Faculty Publications (67)
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- Article
Present Bias Causes and Then Dissipates Auto-enrollment Savings Effects
By: John Beshears, James J. Choi, David Laibson and Peter Maxted
Present bias causes procrastination, which leads households to stick with auto-enrollment defaults. However, present bias also engenders overconsumption. Separation from each employer generates a rollover of 401(k) balances to an individual retirement account (IRA)... View Details
Keywords: Present Bias; Procrastination; Personal Finance; Decision Making; Social Psychology; Retirement
Beshears, John, James J. Choi, David Laibson, and Peter Maxted. "Present Bias Causes and Then Dissipates Auto-enrollment Savings Effects." AEA Papers and Proceedings 112 (May 2022): 136–141.
- September 2020 (Revised July 2022)
- Technical Note
Algorithmic Bias in Marketing
By: Ayelet Israeli and Eva Ascarza
This note focuses on algorithmic bias in marketing. First, it presents a variety of marketing examples in which algorithmic bias may occur. The examples are organized around the 4 P’s of marketing – promotion, price, place and product—characterizing the marketing... View Details
Keywords: Algorithmic Data; Race And Ethnicity; Promotion; ＂Marketing Analytics＂; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analysis; Data Analytics; E-Commerce Strategy; Discrimination; Targeting; Targeted Advertising; Pricing Algorithms; Ethical Decision Making; Customer Heterogeneity; Marketing; Race; Ethnicity; Gender; Diversity; Prejudice and Bias; Marketing Communications; Analytics and Data Science; Analysis; Decision Making; Ethics; Customer Relationship Management; E-commerce; Retail Industry; Apparel and Accessories Industry; United States
Israeli, Ayelet, and Eva Ascarza. "Algorithmic Bias in Marketing." Harvard Business School Technical Note 521-020, September 2020. (Revised July 2022.)
- September 2020 (Revised July 2022)
- Teaching Note
Algorithmic Bias in Marketing
By: Ayelet Israeli and Eva Ascarza
Teaching Note for HBS No. 521-020. This note focuses on algorithmic bias in marketing. First, it presents a variety of marketing examples in which algorithmic bias may occur. The examples are organized around the 4 P’s of marketing – promotion, price, place and... View Details
- Article
Fighting Bias on the Front Lines
By: Alexandra C. Feldberg and Tami Kim
Most companies aim for exceptional customer service, but too few are attentive to the subtle discrimination by frontline employees that can alienate customers, lead to lawsuits, or even cause lasting brand damage by going viral.
This article presents research... View Details
This article presents research... View Details
Keywords: Customer Service; Customer Focus and Relationships; Service Delivery; Diversity; Prejudice and Bias; Organizational Change and Adaptation
Feldberg, Alexandra C., and Tami Kim. "Fighting Bias on the Front Lines." Harvard Business Review 99, no. 6 (November–December 2021): 90–98.
- 2023
- Working Paper
Feature Importance Disparities for Data Bias Investigations
By: Peter W. Chang, Leor Fishman and Seth Neel
It is widely held that one cause of downstream bias in classifiers is bias present in the training data. Rectifying such biases may involve context-dependent interventions such as training separate models on subgroups, removing features with bias in the collection... View Details
Chang, Peter W., Leor Fishman, and Seth Neel. "Feature Importance Disparities for Data Bias Investigations." Working Paper, March 2023.
- July–September 2018
- Article
Memory Bias in Observer-Performance Literature
By: Tamara M. Haygood, Samantha N. Smith and Jia Sun
The objective of our study was to determine how authors of published observer–performance experiments dealt with memory bias in study design. We searched American Journal of Roentgenology online and Radiology using “observer study” and “observer performance.” We... View Details
Haygood, Tamara M., Samantha N. Smith, and Jia Sun. "Memory Bias in Observer-Performance Literature." Art. 031412. Journal of Medical Imaging 5, no. 3 (July–September 2018).
- 2023
- Working Paper
Complexity and Hyperbolic Discounting
By: Benjamin Enke, Thomas Graeber and Ryan Oprea
A large literature shows that people discount financial rewards hyperbolically instead of exponentially. While discounting of money has been questioned as a measure of time preferences, it continues to be highly relevant in empirical practice and predicts a wide range... View Details
Keywords: Hyperbolic Discounting; Present Bias; Bounded Rationality; Cognitive Uncertainty; Behavioral Finance
Enke, Benjamin, Thomas Graeber, and Ryan Oprea. "Complexity and Hyperbolic Discounting." Harvard Business School Working Paper, No. 24-048, February 2024.
- Research Summary
Analyst Disagreement, Forecast Bias and Stock Returns
We present evidence of inefficient information processing in
equity markets by documenting that biases in analysts' earnings
forecasts are reflected in stock prices. In particular, investors
fail to account for analysts' tendency to withhold negative views
and to issue... View Details
- 28 Feb 2022
- Research & Ideas
How Racial Bias Taints Customer Service: Evidence from 6,000 Hotels
when underlying biases are creeping into their interactions with customers. Feldberg and Kim have a few suggestions to help organizations identify whether bias is present and reverse the pattern: Conduct... View Details
Keywords: by Pamela Reynolds
- 2023
- Working Paper
Fintech to the (Worker) Rescue: Access to Earned Wages, Financial Health and Employee Turnover
By: Jose Murillo, Boris Vallée and Dolly Yu
Using novel data from a Mexican FinTech firm, we study the usage by workers of earned wages access, an innovative financial service offered by firms to their employees as a benefit. We find usage to be significant and concentrated towards the end of the pay cycle. We... View Details
Keywords: Fintech; Present Bias; Earned Wage Access; Wages; Employees; Retention; Well-being; Mexico
Murillo, Jose, Boris Vallée, and Dolly Yu. "Fintech to the (Worker) Rescue: Access to Earned Wages, Financial Health and Employee Turnover." Working Paper, 2023.
- Article
Choice Architects Reveal a Bias Toward Positivity and Certainty
By: David P. Daniels and Julian Zlatev
Biases influence important decisions, but little is known about whether and how individuals try to exploit others’ biases in strategic interactions. Choice architects—that is, people who present choices to others—must often decide between presenting choice sets with... View Details
Keywords: Nudges; Biases; Strategic Decision Making; Social Influence; Choice Architects; Choice Architecture; Reflection Effect; Certainty Effect; Loss Aversion; Decision Making; Risk and Uncertainty; Power and Influence
Daniels, David P., and Julian Zlatev. "Choice Architects Reveal a Bias Toward Positivity and Certainty." Organizational Behavior and Human Decision Processes 151 (March 2019): 132–149.
- 07 Mar 2023
- HBS Case
ChatGPT: Did Big Tech Set Up the World for an AI Bias Disaster?
year detailing Gebru’s efforts within Google to urge caution with AI, saying tech companies shouldn’t race to launch systems without considering the potential risks and harms they could cause. She warned that unchecked AI databases could reek of View Details
- 16 Dec 2022
- Research & Ideas
Why Technology Alone Can't Solve AI's Bias Problem
human toll to letting algorithms do the work. “Maybe there is a bias from people who have been traditionally hiring men.” Searches on popular recruiting sites might seem like a neutral way to find prospective candidates, but their... View Details
- February 2010
- Article
Conflict of Interest and the Intrusion of Bias
By: Don A. Moore, Lloyd Tanlu and Max Bazerman
This paper presents evidence of performance persistence in entrepreneurship. We show that entrepreneurs with a track record of success are much more likely to succeed than first-time entrepreneurs and those who have previously failed. In particular, they exhibit... View Details
Keywords: Conflict of Interests; Prejudice and Bias; Performance; Entrepreneurship; Market Timing; Competency and Skills; Perception; Business Startups; Resource Allocation
Moore, Don A., Lloyd Tanlu, and Max Bazerman. "Conflict of Interest and the Intrusion of Bias." Judgment and Decision Making 5, no. 1 (February 2010): 37–53.
- October 2021
- Article
Fintech Borrowers: Lax Screening or Cream-Skimming?
By: Marco Di Maggio and Vincent Yao
Personal credit is the fastest-growing segment of the consumer credit market, mainly driven by fintech lenders' staggering expansion. We study this market using a unique individual-level data, which covers most of the top fintech and traditional lenders, and provides... View Details
Keywords: Fintech; Lending; Consumer Finance; Credit History; Self-control; Present Bias; Financing and Loans; Personal Finance; Credit; Behavior
Di Maggio, Marco, and Vincent Yao. "Fintech Borrowers: Lax Screening or Cream-Skimming?" Review of Financial Studies 34, no. 10 (October 2021): 4565–4618. (LEAD ARTICLE and EDITOR'S CHOICE.)
- 30 May 2024
- Research & Ideas
Racial Bias Might Be Infecting Patient Portals. Can AI Help?
messages. That’s part of the reason the authors say there may be other factors besides direct racial bias driving the results—and a key reason that they are keen to explore this data in future research. One potential factor they... View Details
- 2021
- Working Paper
Time Dependence and Preference: Implications for Compensation Structure and Shift Scheduling
By: Doug J. Chung, Byungyeon Kim and Byoung G. Park
This study jointly examines agents’ time dependence—period effects within instantaneous utility—and time preference—behavior on discounting future utility. The study considers the start- and end-of-period effects for time dependence and exponential and hyperbolic... View Details
Keywords: Time Preferences; Present Bias; Hyperbolic Discounting; Compensation; Dynamic Structural Models; Identification; Time Management; Motivation and Incentives; Behavior; Performance; Compensation and Benefits
Chung, Doug J., Byungyeon Kim, and Byoung G. Park. "Time Dependence and Preference: Implications for Compensation Structure and Shift Scheduling." Harvard Business School Working Paper, No. 21-121, April 2021.
- September 2019
- Case
Sonia Millar: Negotiating for the C-Suite
By: Joshua D. Margolis and Anne Donnellon
This case addresses the nuances of gender dynamics and career progression at the top of the organization, where even women who have strong leadership expertise, experience, and alliances with powerful male colleagues still get stuck. Told from the point of view of... View Details
Keywords: Executives; CEO; Promotion; Gender Bias; Personal Development and Career; Gender; Diversity; Power and Influence
Margolis, Joshua D., and Anne Donnellon. "Sonia Millar: Negotiating for the C-Suite." Harvard Business School Brief Case 920-555, September 2019.
- Article
The Mixed Effects of Online Diversity Training
By: Edward H. Chang, Katherine L. Milkman, Dena M. Gromet, Robert W. Rebele, Cade Massey, Angela L. Duckworth and Adam M. Grant
We present results from a large (n = 3,016) field experiment at a global organization testing whether a brief science-based online diversity training can change attitudes and behaviors toward
women in the workplace. Our preregistered field experiment included an... View Details
Chang, Edward H., Katherine L. Milkman, Dena M. Gromet, Robert W. Rebele, Cade Massey, Angela L. Duckworth, and Adam M. Grant. "The Mixed Effects of Online Diversity Training." Proceedings of the National Academy of Sciences 116, no. 16 (April 16, 2019): 7778–7783.
- September 17, 2021
- Article
AI Can Help Address Inequity—If Companies Earn Users' Trust
By: Shunyuan Zhang, Kannan Srinivasan, Param Singh and Nitin Mehta
While companies may spend a lot of time testing models before launch, many spend too little time considering how they will work in the wild. In particular, they fail to fully consider how rates of adoption can warp developers’ intent. For instance, Airbnb launched a... View Details
Keywords: Artificial Intelligence; Algorithmic Bias; Technological Innovation; Perception; Diversity; Equality and Inequality; Trust; AI and Machine Learning
Zhang, Shunyuan, Kannan Srinivasan, Param Singh, and Nitin Mehta. "AI Can Help Address Inequity—If Companies Earn Users' Trust." Harvard Business Review Digital Articles (September 17, 2021).