Publications
Publications
Show Results For
- All HBS Web
(1,137)
- Faculty Publications (329)
bias
→
- December 4, 2023
- Article
Stop Assuming Introverts Aren't Passionate About Work
By: Kai Krautter, Anabel Büchner and Jon M. Jachimowicz
Society often assumes that the only way to be passionate is to act extroverted, but that is simply not true. In their new research, the authors found that regardless of their actual level of passion, extroverted employees are perceived as more passionate than... View Details
- December 2023
- Article
Brokerage Relationships and Analyst Forecasts: Evidence from the Protocol for Broker Recruiting
By: Braiden Coleman, Michael Drake, Joseph Pacelli and Brady Twedt
In this study, we offer novel evidence on how the nature of brokerage-client relationships can influence the quality of equity research. We exploit a unique setting provided by the Protocol for Broker Recruiting to examine whether relaxed broker non-compete agreement... View Details
Keywords: Brokers; Analysts; Forecasts; Bias; Protocol; Investment; Research; Forecasting and Prediction
- 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
- November 2023
- Article
Brokerage House Initial Public Offerings and Analyst Forecast Quality
By: Mark Bradshaw, Michael Drake, Joseph Pacelli and Brady Twedt
We examine how brokerage firm initial public offerings (IPOs) influence the research quality of sell-side analysts employed by the brokerage. Our main results focus on earnings forecast bias and absolute forecast errors as proxies for research quality. Using a... View Details
- November–December 2023
- Article
Look the Part? The Role of Profile Pictures in Online Labor Markets
By: Isamar Troncoso and Lan Luo
Profile pictures are a key component of many freelancing platforms, a design choice that can impact hiring and matching outcomes. In this paper, we examine how appearance-based perceptions of a freelancer’s fit for the job (i.e., whether a freelancer "looks the part"... View Details
- October 2023
- Case
Making Progress at Progress Software (A)
By: Katherine Coffman, Hannah Riley Bowles and Alexis Lefort
In this case, the Human Capital team at Progress Software has identified that some employees have a hard time understanding how to advance within Progress. This realization leads the team to develop several major people-process innovations: the introduction of... View Details
- October 2023
- Teaching Note
Timnit Gebru: 'SILENCED No More' on AI Bias and The Harms of Large Language Models
By: Tsedal Neeley and Tim Englehart
Teaching Note for HBS Case No. 422-085. Dr. Timnit Gebru—a leading artificial intelligence (AI) computer scientist and co-lead of Google’s Ethical AI team—was messaging with one of her colleagues when she saw the words: “Did you resign?? Megan sent an email saying that... View Details
- October 2023
- Supplement
Making Progress at Progress Software (B)
By: Katherine Coffman, Hannah Riley Bowles and Alexis Lefort
In this case, the Human Capital team at Progress Software has identified that some employees have a hard time understanding how to advance within Progress. This realization leads the team to develop several major people-process innovations: the introduction of... View Details
- 2023
- Working Paper
Causal Interpretation of Structural IV Estimands
By: Isaiah Andrews, Nano Barahona, Matthew Gentzkow, Ashesh Rambachan and Jesse M. Shapiro
We study the causal interpretation of instrumental variables (IV) estimands of nonlinear, multivariate structural models with respect to rich forms of model misspecification. We focus on guaranteeing that the researcher's estimator is sharp zero consistent, meaning... View Details
Keywords: Mathematical Methods
- September 29, 2023
- Article
Eliminating Algorithmic Bias Is Just the Beginning of Equitable AI
By: Simon Friis and James Riley
When it comes to artificial intelligence and inequality, algorithmic bias rightly receives a lot of attention. But it’s just one way that AI can lead to inequitable outcomes. To truly create equitable AI, we need to consider three forces through which it might make... View Details
- September 2023
- Exercise
Irrationality in Action: Decision-Making Exercise
By: Alison Wood Brooks, Michael I. Norton and Oliver Hauser
This teaching exercise highlights the obstacle of biases in decision-making, allowing students to generate examples of potentially poor decision-making rooted in abundant and unwanted bias. This exercise has two parts: a pre-class, online survey in which students... View Details
- 2024
- Working Paper
Second- versus Third-party Audit Quality: Evidence from Global Supply Chain Monitoring
By: Maria R. Ibanez, Ashley Palmarozzo, Jodi L. Short and Michael W. Toffel
Capitalizing on the superior credibility and flexibility and potential lower cost of external assessments, many global buyers are relying less on their own employee (“second-party”) auditors and more on third-party auditors to monitor and prevent environmental and... View Details
- June 2023
- Simulation
Artea Dashboard and Targeting Policy Evaluation
By: Ayelet Israeli and Eva Ascarza
Companies deploy A/B experiments to gain valuable insights about their customers in order to answer strategic business problems. In marketing, A/B tests are often used to evaluate marketing interventions intended to generate incremental outcomes for the firm. The Artea... View Details
Keywords: Algorithm Bias; Algorithmic Data; Race And Ethnicity; Experimentation; Promotion; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analysis; Data Analytics; E-Commerce Strategy; Discrimination; Targeted Advertising; Targeted Policies; Pricing Algorithms; A/B Testing; Ethical Decision Making; Customer Base Analysis; Customer Heterogeneity; Coupons; Marketing; Race; Gender; Diversity; Customer Relationship Management; Marketing Communications; Advertising; Decision Making; Ethics; E-commerce; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; United States
- 2023
- Working Paper
Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness
By: Neil Menghani, Edward McFowland III and Daniel B. Neill
In this paper, we develop a new criterion, "insufficiently justified disparate impact" (IJDI), for assessing whether recommendations (binarized predictions) made by an algorithmic decision support tool are fair. Our novel, utility-based IJDI criterion evaluates false... View Details
- 2024
- Working Paper
Black Empowerment and White Mobilization: The Effects of the Voting Rights Act
By: Andrea Bernini, Giovanni Facchini, Marco Tabellini and Cecilia Testa
How did southern whites respond to the 1965 Voting Rights Act (VRA)? Leveraging
newly digitized data on county-level voter registration by race between 1956 and
1980, and exploiting pre-determined variation in exposure to the federal intervention,
we document that... View Details
- June 2023
- Article
Amplification of Emotion on Social Media
By: Amit Goldenberg and Robb Willer
Why do expressions of emotion seem so heightened on social media? Brady et al. argue that extreme moral outrage on social media is not only driven by the producers and sharers of emotional expressions, but also by systematic biases in the way people that perceive moral... View Details
- 2023
- Working Paper
Auditing Predictive Models for Intersectional Biases
By: Kate S. Boxer, Edward McFowland III and Daniel B. Neill
Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for individuals at the intersection of two or more protected classes. To address this risk, we... View Details
- 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
- May 9, 2023
- Article
8 Questions About Using AI Responsibly, Answered
By: Tsedal Neeley
Generative AI tools are poised to change the way every business operates. As your own organization begins strategizing which to use, and how, operational and ethical considerations are inevitable. This article delves into eight of them, including how your organization... View Details
- 2023
- Working Paper
Setting Gendered Expectations? Recruiter Outreach Bias in Online Tech Training Programs
By: Jacqueline N. Lane, Karim R. Lakhani and Roberto Fernandez
Competence development in digital technologies, analytics, and artificial intelligence is increasingly important to all types of organizations and their workforce. Universities and corporations are investing heavily in developing training programs, at all tenure... View Details