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- Faculty Publications (31)
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- All HBS Web (69)
- Faculty Publications (31)
- 01 Dec 2023
- News
Thinking Ahead
As we wind down 2023, there’s talk everywhere of generative AI and how it will fundamentally alter the world as we know it; but how does that translate for your corner of the business world? Is TikTok something you need to take seriously? (Is it time to dance?) We... View Details
- Web
Human Behavior & Decision-Making - Faculty & Research
emphasizing human-AI collaboration to achieve radical insights. Keywords: Large Language Model ; AI and Machine Learning ; Creativity ; Innovation Strategy Citation Read Now Purchase Related De Freitas, Julian, G. Nave, and Stefano... View Details
- 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
- 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 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
Renewable Generation and Energy Storage Investments” with Simone Marinesi, and Serguei Netessine. Himabindu Lakkaraju : Honorable Mention for the Workshop on Trustworthy and Socially Responsible Machine Learning (TSRML) Outstanding Paper... 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.
- 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
- 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.
- 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.
- 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
- 15 Nov 2024
- News
Driving Change
how the use of AI will disrupt labor markets or that it may make humans lazier, Feinzaig said she believes it will make us more human. “Since the Industrial Revolution, the focus has been on turning humans into machines to create... View Details
- Web
Launching Tech Ventures - Course Catalog
design and run as well as the organizations they build. LTV has a tactical, implementation bias rather than a strategic one. There is a modest overlap with Product Management and Entrepreneurial Sales, but LTV is solely focused on... View Details