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Show Results For
- All HBS Web
(1,145)
- News (193)
- Research (741)
- Events (8)
- Multimedia (18)
- Faculty Publications (496)
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- 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.
- 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.
- 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)
- 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; Retail Industry; Apparel and Accessories Industry; Technology 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.)
- 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.
- 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).
- 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.)
- 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.
- 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
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.
- 2009
- Dictionary Entry
Negativity Bias
By: Todd Rogers and Max H. Bazerman
Keywords: Prejudice and Bias
- 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.
- 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; Service 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.)
- June 2020
- Article
How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections
By: Maria Ibanez and Michael W. Toffel
Accuracy and consistency are critical for inspections to be an effective, fair, and useful tool for assessing risks, quality, and suppliers—and for making decisions based on those assessments. We examine how inspector schedules could introduce bias that erodes... View Details
Keywords: Assessment; Bias; Inspection; Scheduling; Econometric Analysis; Empirical Research; Regulation; Health; Food; Safety; Quality; Performance Consistency; Governing Rules, Regulations, and Reforms
Ibanez, Maria, and Michael W. Toffel. "How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections." Management Science 66, no. 6 (June 2020): 2396–2416. (Revised February 2019. Featured in Harvard Business Review, Forbes, Food Safety Magazine, Food Safety News, and KelloggInsight. (2020 MSOM Responsible Research Finalist.))
- October 2024
- Article
Sampling Bias in Entrepreneurial Experiments
By: Ruiqing Cao, Rembrand Koning and Ramana Nanda
Using data from a prominent online platform for launching new digital products, we document that ‘sampling bias’—defined as the difference between a startup’s target customer base and the actual sample on which early ‘beta tests’ are conducted—has a systematic and... View Details
Cao, Ruiqing, Rembrand Koning, and Ramana Nanda. "Sampling Bias in Entrepreneurial Experiments." Management Science 70, no. 10 (October 2024): 7283–7307.
- 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.
- Article
Optimality Bias in Moral Judgment
By: Julian De Freitas and Samuel G.B. Johnson
We often make decisions with incomplete knowledge of their consequences. Might people nonetheless expect others to make optimal choices, despite this ignorance? Here, we show that people are sensitive to moral optimality: that people hold moral agents accountable... View Details
Keywords: Moral Judgment; Lay Decision Theory; Theory Of Mind; Causal Attribution; Moral Sensibility; Decision Making
De Freitas, Julian, and Samuel G.B. Johnson. "Optimality Bias in Moral Judgment." Journal of Experimental Social Psychology 79 (November 2018): 149–163.
- 04 Mar 2019
- Working Paper Summaries
The Revision Bias
- Article
Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)
By: Eva Ascarza and Ayelet Israeli
An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”... View Details
Keywords: Algorithm Bias; Personalization; Targeting; Generalized Random Forests (GRF); Discrimination; Customization and Personalization; Decision Making; Fairness; Mathematical Methods
Ascarza, Eva, and Ayelet Israeli. "Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)." e2115126119. Proceedings of the National Academy of Sciences 119, no. 11 (March 8, 2022).
- Article
Deep Down My Enemy Is Good: Thinking about the True Self Reduces Intergroup Bias
By: Julian De Freitas and Mina Cikara
Intergroup bias—preference for one's in-group relative to out-groups—is one of the most robust phenomena in all of psychology. Here we investigate whether a positive bias that operates at the individual-level, belief in a good true self, may be leveraged to reduce... View Details
De Freitas, Julian, and Mina Cikara. "Deep Down My Enemy Is Good: Thinking about the True Self Reduces Intergroup Bias." Journal of Experimental Social Psychology 74 (January 2018): 307–316.