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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
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.
- 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.
- January 1982
- Article
A Negativity Bias in Interpersonal Evaluation
By: T. M. Amabile and A. H. Glazebrook
Two studies were conducted to demonstrate a bias toward negativity in evaluations of persons or their work in particular social circumstances. In Study 1, subjects evaluated materials written by peers. Those working under conditions that placed them in low status... View Details
Keywords: Social Psychology; Status and Position; Prejudice and Bias; Performance Evaluation; Situation or Environment; Perception; Attitudes
Amabile, T. M., and A. H. Glazebrook. "A Negativity Bias in Interpersonal Evaluation." Journal of Experimental Social Psychology 18 (January 1982): 1–22.
- 21 Jul 2020
- Cold Call Podcast
Starbucks Commits to Raising Awareness of Racial 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.
- 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).
- 09 Aug 2022
- Cold Call Podcast
A Lesson from Google: Can AI Bias be Monitored Internally?
Keywords: Re: Tsedal Neeley
- 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.
- 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
nothing to improve here.’ But what our results are showing is that we need to go beyond that because, even if they are responding to everyone, it doesn't mean that everyone is getting treated equally.” Advice for detecting bias... View Details
Keywords: by Pamela Reynolds
- 2023
- Article
Provable Detection of Propagating Sampling Bias in Prediction Models
By: Pavan Ravishankar, Qingyu Mo, Edward McFowland III and Daniel B. Neill
With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider... View Details
Ravishankar, Pavan, Qingyu Mo, Edward McFowland III, and Daniel B. Neill. "Provable Detection of Propagating Sampling Bias in Prediction Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9562–9569. (Presented at the 37th AAAI Conference on Artificial Intelligence (2/7/23-2/14/23) in Washington, DC.)
- 18 Oct 2004
- Research & Ideas
The Bias of Wall Street Analysts
If it's one lesson the individual investor learned the hard way from the collapse of Enron, it is that the recommendations of Wall Street stock analysts can be influenced by much more than purely objective research. Just look at the large number of analysts who kept... View Details
- 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
- January–February 2019
- Article
Who Loses When a Team Wins? Better Performance Increases Racial Bias
By: Letian Zhang
Although it is well known that organizational and team performance influences strategic decision-making, little is known about its impact on ascriptive inequality. This study proposes a performance effect on racial bias: higher team performance reduces managers’... View Details
Keywords: Discrimination; Race And Ethnicity; Performance Feedback; NBA; Prejudice and Bias; Race; Ethnicity; Performance; Sports
Zhang, Letian. "Who Loses When a Team Wins? Better Performance Increases Racial Bias." Organization Science 30, no. 1 (January–February 2019): 40–50.
- March 2013
- Article
The Client Is King: Do Mutual Fund Relationships Bias Analyst Recommendations?
By: Michael Firth, Chen Lin, Ping Liu and Yuhai Xuan
This paper investigates whether the business relations between mutual funds and brokerage firms influence sell-side analyst recommendations. Using a unique data set that discloses brokerage firms' commission income derived from each mutual fund client as well as the... View Details
Firth, Michael, Chen Lin, Ping Liu, and Yuhai Xuan. "The Client Is King: Do Mutual Fund Relationships Bias Analyst Recommendations?" Journal of Accounting Research 51, no. 1 (March 2013): 165–200.
- 2015
- Working Paper
Expertise vs. Bias in Evaluation: Evidence from the NIH
By: Danielle Li
Evaluators with expertise in a particular field may have an informational advantage in separating good projects from bad. At the same time, they may also have personal preferences that impact their objectivity. This paper develops a framework for separately identifying... View Details
Li, Danielle. "Expertise vs. Bias in Evaluation: Evidence from the NIH." Harvard Business School Working Paper, No. 16-053, October 2015.
- 18 Oct 2022
- Research & Ideas
When Bias Creeps into AI, Managers Can Stop It by Asking the Right Questions
algorithm generates fair outcomes. As the algorithm sorts through information to optimize its objective, BEAT detects and eliminates bias at key points in the training process. For instance, BEAT could help a car service charge surge... View Details
Keywords: by Rachel Layne
- 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