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- All HBS Web (70)
- Faculty Publications (33)
Show Results For
- All HBS Web (70)
- Faculty Publications (33)
- 2024
- Working Paper
Warnings and Endorsements: Improving Human-AI Collaboration Under Covariate Shift
By: Matthew DosSantos DiSorbo and Kris Ferreira
Problem definition: While artificial intelligence (AI) algorithms may perform well on data that are representative of the training set (inliers), they may err when extrapolating on non-representative data (outliers). These outliers often originate from covariate shift,... View Details
DosSantos DiSorbo, Matthew, and Kris Ferreira. "Warnings and Endorsements: Improving Human-AI Collaboration Under Covariate Shift." Working Paper, February 2024.
- Web
Technology & Innovation - Faculty & Research
and unfunded firms. Because randomization of the sample was not feasible, we address endogeneity around selection bias using a sample of qualitatively similar firms based on a funding decision score. This allows us to observe the local... View Details
- 01 Dec 2023
- News
Thinking Ahead
Lab at the Digital Data Design Institute at Harvard; his research develops tools for machine learning that mitigate bias and enhance privacy. Generative AI poses a greater risk to privacy by its nature, Neel... View Details
- Web
Human Behavior & Decision-Making - Faculty & Research
Economy ; Airbnb ; Image Feature Extraction ; Machine Learning ; Facial Expressions ; Prejudice and Bias ; Nonverbal Communication ; E-commerce ; Consumer Behavior ; Perception Citation Read Now Related... View Details
- 30 May 2023
- Research & Ideas
Can AI Predict Whether Shoppers Would Pick Crest or Colgate?
came from a sample of customers.” While the recent emergence of ChatGPT has reignited fears that machines may replace humans in the workplace, the results of this study don’t necessarily mean that AI is going to gut marketing departments,... View Details
Keywords: by Kristen Senz
- 07 Jan 2019
- Research & Ideas
The Better Way to Forecast the Future
for prediction and for forecasting something that is unknown.” The rise of big data and machine learning offers infinitely more fuel to churn out probability forecasts, which can serve as an entry point for businesses looking to harness... View Details
- 07 Aug 2013
- What Do You Think?
Is There Still a Role for Judgment in Decision-Making?
'gut check' on big decisions is always prudent. I realize that is the sort of bias these authors warn about, but the application of their methods shouldn't reduce decision-making to a formula " Phil Clark had a more encompassing view... View Details
Keywords: by James Heskett
- 26 Mar 2018
- Research & Ideas
To Motivate Employees, Give an Unexpected Bonus (or Penalty)
employees make or how many units they produce. “The objective performance measures don’t take into consideration whether the machine broke down or whether someone is still learning the job,” Gallani explains. To compensate, managers often... View Details
- Blog
Is AI Coming for Your Job?
be displaced in large numbers. Those job losses will be partially offset by job gains for machine learning specialists and emerging jobs like prompt engineers. But, once companies learn how to exploit generative AI, we can anticipate... View Details
- Web
Judges - Alumni
expertise with a start-up owner mindset and a bias toward action and outcomes. A trusted partner and “first call” to entrepreneurs, Tina currently serves on the boards of Arrae, August, and Dae. Tina is uniquely fluent across the... View Details
- 02 Mar 2016
- News
David Moss is Rewriting History
between the board’s conservative Republicans, who perceived a liberal bias in the curriculum, and its more moderate Republicans and Democrats. For three days, the board held contentious open meetings, arguing issues centuries old—Were the... View Details
Keywords: April White
- May 2022 (Revised June 2024)
- Case
LOOP: Driving Change in Auto Insurance Pricing
By: Elie Ofek and Alicia Dadlani
John Henry and Carey Anne Nadeau, co-founders and co-CEOs of LOOP, an insurtech startup based in Austin, Texas, were on a mission to modernize the archaic $250 billion automobile insurance market. They sought to create equitably priced insurance by eliminating pricing... View Details
Keywords: AI and Machine Learning; Technological Innovation; Equality and Inequality; Prejudice and Bias; Growth and Development Strategy; Customer Relationship Management; Price; Insurance Industry; Financial Services Industry
Ofek, Elie, and Alicia Dadlani. "LOOP: Driving Change in Auto Insurance Pricing." Harvard Business School Case 522-073, May 2022. (Revised June 2024.)
- Web
Technology & Operations Management Awards & Honors - Faculty & Research
: Honorable Mention for the Workshop on Trustworthy and Socially Responsible Machine Learning (TSRML) Outstanding Paper Award at the 2022 Conference on Neural Information Processing Systems (NeurIPS). Himabindu Lakkaraju : Winner of the... View Details
- 2021
- Chapter
Towards a Unified Framework for Fair and Stable Graph Representation Learning
By: Chirag Agarwal, Himabindu Lakkaraju and Marinka Zitnik
As the representations output by Graph Neural Networks (GNNs) are increasingly employed in real-world applications, it becomes important to ensure that these representations are fair and stable. In this work, we establish a key connection between counterfactual... View Details
Agarwal, Chirag, Himabindu Lakkaraju, and Marinka Zitnik. "Towards a Unified Framework for Fair and Stable Graph Representation Learning." In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence, edited by Cassio de Campos and Marloes H. Maathuis, 2114–2124. AUAI Press, 2021.
- 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
Menghani, Neil, Edward McFowland III, and Daniel B. Neill. "Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness." Working Paper, June 2023.
- 27 Jun 2024
- Research & Ideas
Gen AI Marketing: How Some 'Gibberish' Code Can Give Products an Edge
their products listed on top, is that a good thing or a bad thing? It just depends on which side you’re looking from,” says Lakkaraju. The coffee machine experiment The study involves a hypothetical search for an “affordable” new coffee... View Details
- 01 Mar 2005
- News
Facing Ambiguity
information,” notes Roberto. “This wasn’t just people shuffling paper in Houston. They were monitoring astronauts in space, and the foam strike was one small issue in a complex set of events.” “It’s real information and real people in real time,” Edmondson adds.... View Details
- 10 Mar 2011
- What Do You Think?
To What Degree Does the Job Make the Person?
new job. In other words, is there a self-selection bias in studies of the effects of job on a person's chemical makeup? As Stephanie Smith put it, "Perhaps it's a case of either the hormones and natural adaptability of the person... View Details
Keywords: by James Heskett
- Web
BiGS Fellows | Institute for Business in Global Society
collects data about the outcome of an algorithm within a particular context, and then assesses its impact on its users. The platform studies facial and emotion recognition and can help uncover the racial bias in algorithms used by social... View Details
- 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.