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- News (13)
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- Article
Overcoming the Outcome Bias: Making Intentions Matter
People often make the well-documented mistake of paying too much attention to the outcomes of others’ actions while neglecting information about the original intentions leading to those outcomes. In five experiments, we examine interventions aimed at reducing this... View Details
Keywords: Outcome Bias; Intentions; Joint Evaluation; Judgment; Separate Evaluation; Goals and Objectives; Prejudice and Bias; Judgments; Performance Evaluation; Outcome or Result
Sezer, Ovul, Ting Zhang, Francesca Gino, and Max Bazerman. "Overcoming the Outcome Bias: Making Intentions Matter." Organizational Behavior and Human Decision Processes 137 (November 2016): 13–26.
- 01 Apr 2008
- Working Paper Summaries
No Harm, No Foul: The Outcome Bias in Ethical Judgments
- 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.))
- 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.
- 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.
- 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.
- 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
- 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.
- Article
Physician–patient Racial Concordance and Disparities in Birthing Mortality for Newborns
By: Brad N. Greenwood, Rachel R. Hardeman, Laura Huang and Aaron Sojourner
Recent work has emphasized the benefits of patient–physician concordance on clinical care outcomes for underrepresented minorities, arguing it can ameliorate outgroup biases, boost communication, and increase trust. We explore concordance in a setting where racial... View Details
Greenwood, Brad N., Rachel R. Hardeman, Laura Huang, and Aaron Sojourner. "Physician–patient Racial Concordance and Disparities in Birthing Mortality for Newborns." Proceedings of the National Academy of Sciences 117, no. 35 (September 1, 2020): 21194–21200.
- 18 Oct 2004
- Research & Ideas
The Bias of Wall Street Analysts
when investors will disregard stock research entirely? And if they did, what other forms of analysis would they likely find more reliable? A: This would be a great outcome indeed, but probably for different reasons than intended by the... View Details
- 18 Oct 2022
- Research & Ideas
When Bias Creeps into AI, Managers Can Stop It by Asking the Right Questions
look at the outcomes of these algorithms to detect these biases in the first place. “The positive outlook here is—if you compare algorithmic bias to human bias—with algorithmic bias, you can at least offer... View Details
Keywords: by Rachel Layne
- Summer 2021
- Article
Predictable Country-level Bias in the Reporting of COVID-19 Deaths
By: Botir Kobilov, Ethan Rouen and George Serafeim
We examine whether a country’s management of the COVID-19 pandemic relate to the downward biasing of the number of reported deaths from COVID-19. Using deviations from historical averages of the total number of monthly deaths within a country, we find that the... View Details
Keywords: COVID-19; Deaths; Reporting; Incentives; Government Policy; Health Pandemics; Health Care and Treatment; Country; Crisis Management; Outcome or Result; Reports; Policy
Kobilov, Botir, Ethan Rouen, and George Serafeim. "Predictable Country-level Bias in the Reporting of COVID-19 Deaths." Journal of Government and Economics 2 (Summer 2021).
- 2023
- Working Paper
The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities
By: David S. Scharfstein and Sergey Chernenko
We show that the use of algorithms to predict race has significant limitations in measuring and understanding the sources of racial disparities in finance, economics, and other contexts. First, we derive theoretically the direction and magnitude of measurement bias in... View Details
Keywords: Racial Disparity; Paycheck Protection Program; Measurement Error; AI and Machine Learning; Race; Measurement and Metrics; Equality and Inequality; Prejudice and Bias; Forecasting and Prediction; Outcome or Result
Scharfstein, David S., and Sergey Chernenko. "The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities." Working Paper, April 2023.
- 15 Jul 2009
- Working Paper Summaries
Policy Bundling to Overcome Loss Aversion: A Method for Improving Legislative Outcomes
- Article
(Too) Optimistic about Optimism: The Belief that Optimism Improves Performance.
By: Elizabeth R. Tenney, Jennifer M. Logg and Don A Moore
A series of experiments investigated why people value optimism and whether they are right to do so. In Experiments 1A and 1B, participants prescribed more optimism for someone implementing decisions than for someone deliberating, indicating that people prescribe... View Details
Keywords: Optimism; Bias; Accuracy; Decision Phase; Performance; Attitudes; Performance Improvement; Perception; Outcome or Result
Tenney, Elizabeth R., Jennifer M. Logg, and Don A Moore. "(Too) Optimistic about Optimism: The Belief that Optimism Improves Performance." Journal of Personality and Social Psychology 108, no. 3 (March 2015): 377–399. (lead article.)
- Article
Price and Quality Decisions by Self-Serving Managers
By: Marco Bertini, Daniel Halbheer and Oded Koenigsberg
We present a theory of price and quality decisions by managers who are self-serving. In the theory, firms stress the price or quality of their products, but not both. Accounting for this, managers exploit any uncertainty about the cause of market outcomes to credit... View Details
Keywords: Causal Reasoning; Self-serving Bias; Strategic Orientation; Managerial Decision-making; Price; Quality; Decision Making; Theory
Bertini, Marco, Daniel Halbheer, and Oded Koenigsberg. "Price and Quality Decisions by Self-Serving Managers." International Journal of Research in Marketing 37, no. 2 (June 2020): 236–257.
- 2021
- Working Paper
Invisible Primes: Fintech Lending with Alternative Data
By: Marco Di Maggio, Dimuthu Ratnadiwakara and Don Carmichael
We exploit anonymized administrative data provided by a major fintech platform to investigate whether using alternative data to assess borrowers’ creditworthiness results in broader credit access. Comparing actual outcomes of the fintech platform’s model to... View Details
Keywords: Fintech Lending; Alternative Data; Machine Learning; Algorithm Bias; Finance; Information Technology; Financing and Loans; Analytics and Data Science; Credit
Di Maggio, Marco, Dimuthu Ratnadiwakara, and Don Carmichael. "Invisible Primes: Fintech Lending with Alternative Data." Harvard Business School Working Paper, No. 22-024, October 2021.
- 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.
- 2024
- Working Paper
A Gender Backlash: Does Exposure to Female Labor Market Participation Fuel Gender Conservatism?
By: Paula Rettl, Diane Bolet, Catherine E. De Vries, Simone Cremaschi, Tarik Abou-Chadi and Sergi Pardos-Prado
The growing participation of women in the labor market has marked a significant societal transformation, coinciding with the rise of gender conservatism and far-right support. We study whether the economic consequences of labor market feminization and gender backlash... View Details
Keywords: Gender Bias; Gender Equality; Gender Inclusivity; Politics; Political Backlash; Political Culture; Conservatism; Gender; Government and Politics; Equality and Inequality; Prejudice and Bias; Labor
Rettl, Paula, Diane Bolet, Catherine E. De Vries, Simone Cremaschi, Tarik Abou-Chadi, and Sergi Pardos-Prado. "A Gender Backlash: Does Exposure to Female Labor Market Participation Fuel Gender Conservatism?" Harvard Business School Working Paper, No. 25-022, November 2024.