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Show Results For
- All HBS Web
(1,098)
- News (183)
- Research (762)
- Events (8)
- Multimedia (14)
- Faculty Publications (497)
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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.
- Article
Unconscious Bias Training That Works
By: Francesca Gino and Katherine Coffman
To become more diverse, equitable, and inclusive, many companies have turned to unconscious bias (UB) training. By raising awareness of the mental shortcuts that lead to snap judgments—often based on race and gender—about people’s talents or character, it strives to... View Details
Keywords: Implicit Bias; Social Integration; Empathy; Prejudice and Bias; Employees; Training; Attitudes; Behavior; Organizational Change and Adaptation
Gino, Francesca, and Katherine Coffman. "Unconscious Bias Training That Works." Harvard Business Review 99, no. 5 (September–October 2021): 114–123.
- January 2025
- Article
Reducing Prejudice with Counter-stereotypical AI
By: Erik Hermann, Julian De Freitas and Stefano Puntoni
Based on a review of relevant literature, we propose that the proliferation of AI with human-like and social features presents an unprecedented opportunity to address the underlying cognitive and affective drivers of prejudice. An approach informed by the psychology of... View Details
Keywords: Prejudice and Bias; AI and Machine Learning; Interpersonal Communication; Social and Collaborative Networks
Hermann, Erik, Julian De Freitas, and Stefano Puntoni. "Reducing Prejudice with Counter-stereotypical AI." Consumer Psychology Review 8, no. 1 (January 2025): 75–86.
- 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; Apparel and Accessories 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.)
- Article
A Fair Game? Racial Bias and Repeated Interaction between NBA Coaches and Players
By: Letian Zhang
There is strong evidence of racial bias in organizations but little understanding of how it changes with repeated interaction. This study proposes that repeated interaction has the potential to reduce racial bias, but its moderating effects are limited to the treatment... View Details
Keywords: Discrimination; Bias; Interaction; NBA; Prejudice and Bias; Race; Equality and Inequality; Interpersonal Communication; Sports
Zhang, Letian. "A Fair Game? Racial Bias and Repeated Interaction between NBA Coaches and Players." Administrative Science Quarterly 62, no. 4 (December 2017): 603–625.
- May 2014
- Article
Bias in Reduced-form Estimates of Pass-through
By: Alexander MacKay, Nathan H. Miller, Marc Remer and Gloria Sheu
We show that, in general, consistent estimates of cost pass-through are not obtained from reduced-form regressions of price on cost. We derive a formal approximation for the bias that arises even under standard orthogonality conditions. We provide guidance on the... View Details
MacKay, Alexander, Nathan H. Miller, Marc Remer, and Gloria Sheu. "Bias in Reduced-form Estimates of Pass-through." Economics Letters 123, no. 2 (May 2014): 200–202.
- August 2020
- Article
Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation
By: Prithwiraj Choudhury, Evan Starr and Rajshree Agarwal
The use of machine learning (ML) for productivity in the knowledge economy requires considerations of important biases that may arise from ML predictions. We define a new source of bias related to incompleteness in real time inputs, which may result from strategic... View Details
Choudhury, Prithwiraj, Evan Starr, and Rajshree Agarwal. "Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation." Strategic Management Journal 41, no. 8 (August 2020): 1381–1411.
- June 2013
- Article
Opting-in: Participation Bias in Economic Experiments
By: Robert Slonim, Carmen Wang, Ellen Garbarino and Danielle Merrett
Assuming individuals rationally decide whether to participate or not to participate in lab experiments, we hypothesize several non-representative biases in the characteristics of lab participants. We test the hypotheses by first collecting survey and experimental data... View Details
Slonim, Robert, Carmen Wang, Ellen Garbarino, and Danielle Merrett. "Opting-in: Participation Bias in Economic Experiments." Journal of Economic Behavior & Organization 90 (June 2013): 43–70.
- September 2020 (Revised July 2022)
- Exercise
Artea (D): Discrimination through Algorithmic Bias in Targeting
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The... View Details
Keywords: Targeted Advertising; Discrimination; Algorithmic Data; Bias; Advertising; Race; Gender; Marketing; Diversity; Customer Relationship Management; Prejudice and Bias; Analytics and Data Science; Apparel and Accessories Industry; Apparel and Accessories Industry; Apparel and Accessories Industry; United States
Ascarza, Eva, and Ayelet Israeli. "Artea (D): Discrimination through Algorithmic Bias in Targeting." Harvard Business School Exercise 521-043, 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
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).
- 2008
- Chapter
Business Archives and Overcoming Survivor Bias
By: G. Jones
Among the most longstanding criticisms of business history as an academic discipline is the bias caused towards studying successful firms rather than failures, and the related use of longevity as a major criterion for success. The grand narratives of business history... View Details
- 2009
- Dictionary Entry
Negativity Bias
By: Todd Rogers and Max H. Bazerman
Keywords: Prejudice and Bias
- 04 Mar 2019
- Working Paper Summaries
The Revision Bias
- 2011
- Article
Bias in Search Results?: Diagnosis and Response
By: Benjamin Edelman
I explore allegations of search engine bias, including understanding a search engine's incentives to bias results, identifying possible forms of bias, and evaluating methods of verifying whether bias in fact occurs. I then consider possible legal and policy responses,... View Details
Keywords: Prejudice and Bias; Motivation and Incentives; Outcome or Result; Markets; Legal Liability; Policy; Search Technology; Performance Evaluation; Governing Rules, Regulations, and Reforms
Edelman, Benjamin. "Bias in Search Results?: Diagnosis and Response." Indian Journal of Law and Technology 7 (2011): 16–32.
- 2022
- Working Paper
Confidence, Self-Selection and Bias in the Aggregate
By: Benjamin Enke, Thomas Graeber and Ryan Oprea
The influence of behavioral biases on aggregate outcomes like prices and allocations depends in part on self-selection: whether rational people opt more strongly into aggregate interactions than biased individuals. We conduct a series of betting market, auction and... View Details
Enke, Benjamin, Thomas Graeber, and Ryan Oprea. "Confidence, Self-Selection and Bias in the Aggregate." NBER Working Paper Series, No. 30262, July 2022.
- 13 Sep 2013
- News
Educate Everyone About Second-Generation Gender Bias
- 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.
- 2018
- Working Paper
How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections
By: Maria Ibanez and Michael W. Toffel
Many production processes are subject to inspection to ensure they meet quality, safety, and environmental standards imposed by companies and regulators. Inspection accuracy is critical to inspections being a useful input to assessing risks, allocating quality... View Details
Keywords: Assessment; Bias; Inspection; Scheduling; Econometric Analysis; Empirical Research; Regulation; Health; Food; Safety; Quality; Performance Consistency; Performance Evaluation; Food and Beverage Industry; Food and Beverage Industry
Ibanez, Maria, and Michael W. Toffel. "How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections." Harvard Business School Working Paper, No. 17-090, April 2017. (Revised October 2018. Formerly titled "Assessing the Quality of Quality Assessment: The Role of Scheduling". Featured in Forbes, Food Safety Magazine, and Food Safety News.)
- January 2021
- Article
Veil-of-Ignorance Reasoning Mitigates Self-Serving Bias in Resource Allocation During the COVID-19 Crisis
By: Karen Huang, Regan Bernhard, Netta Barak-Corren, Max Bazerman and Joshua D. Greene
The COVID-19 crisis has forced healthcare professionals to make tragic decisions concerning which patients to save. Furthermore, the COVID-19 crisis has foregrounded the influence of self-serving bias in debates on how to allocate scarce resources. A utilitarian... View Details
Keywords: Self-serving Bias; Procedural Justice; Bioethics; COVID-19; Fairness; Health Pandemics; Resource Allocation; Decision Making
Huang, Karen, Regan Bernhard, Netta Barak-Corren, Max Bazerman, and Joshua D. Greene. "Veil-of-Ignorance Reasoning Mitigates Self-Serving Bias in Resource Allocation During the COVID-19 Crisis." Judgment and Decision Making 16, no. 1 (January 2021): 1–19.