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Show Results For
- All HBS Web
(1,277)
- People (1)
- News (234)
- Research (670)
- Events (17)
- Multimedia (8)
- Faculty Publications (563)
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- 05 May 2022
- HBS Case
College Degrees: The Job Requirement Companies Seek, but Don't Really Need
than 70 percent of Black, Latinx, and rural workers from landing jobs, even though they may have the actual skills to do the required work. Many companies rely on machine View Details
Keywords: by Jay Fitzgerald
- October 2024
- Technical Note
Prompt Engineering
By: Michael Parzen and Jo Ellery
This note covers the basics of prompt engineering, a key tool for making use of modern generative AI. We discuss the principles of prompt engineering and illustrate these principles with techniques for asking questions. We further list the types of prompts that can be... View Details
Parzen, Michael, and Jo Ellery. "Prompt Engineering." Harvard Business School Technical Note 625-056, October 2024.
- 17 Apr 2019
- Research & Ideas
How Managers Stifle Creativity
Creative thinking skills. Some people are naturally able to think outside the box. But we can all learn and improve our creative thinking skills. For example, there are techniques that involve using... View Details
Keywords: by Danielle Kost
- 02 Jun 2022
- Research & Ideas
Blissful Thinking: When It Comes to Finding Happiness, 'Your Dreams Are Liars'
the research problem, at this point. I’m the subject and I’m the researcher. I’m like a perpetual-motion machine of happiness research.” In this conversation with the HBS Alumni Bulletin, Brooks talks about... View Details
Keywords: by Dan Morrell
- 23 Nov 2021
- Research & Ideas
The Vinyl Renaissance: Take Those Old Records Off the Shelf
Austin, Texas, in 2017, and Ryan Raffaelli, the Marvin Bower Associate Professor at HBS, to learn more about the vinyl renaissance. The interview has been edited for clarity View Details
- 2023
- Article
Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
By: Suraj Srinivas, Sebastian Bordt and Himabindu Lakkaraju
One of the remarkable properties of robust computer vision models is that their input-gradients are often aligned with human perception, referred to in the literature as perceptually-aligned gradients (PAGs). Despite only being trained for classification, PAGs cause... View Details
Srinivas, Suraj, Sebastian Bordt, and Himabindu Lakkaraju. "Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 30 Oct 2017
- Research & Ideas
Asking Questions Can Get You a Better Job or a Second Date
Source: FangXiaNuo New research suggests that people who ask questions, particularly follow-up questions, may become better managers, land better jobs, and even win second dates. “Compared to those who do not ask many questions, people... View Details
Keywords: by Rachel Layne
- 2023
- Article
MoPe: Model Perturbation-based Privacy Attacks on Language Models
By: Marvin Li, Jason Wang, Jeffrey Wang and Seth Neel
Recent work has shown that Large Language Models (LLMs) can unintentionally leak sensitive information present in their training data. In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training... View Details
Li, Marvin, Jason Wang, Jeffrey Wang, and Seth Neel. "MoPe: Model Perturbation-based Privacy Attacks on Language Models." Proceedings of the Conference on Empirical Methods in Natural Language Processing (2023): 13647–13660.
- 26 Oct 2022
- Research & Ideas
How Paid Promos Take the Shine Off YouTube Stars (and Tips for Better Influencer Marketing)
desired outcome.” Deep dive into YouTube The influencer marketing machine is huge and growing. The industry was reportedly valued at $6 billion in 2020 and is projected to... 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
Boxer, Kate S., Edward McFowland III, and Daniel B. Neill. "Auditing Predictive Models for Intersectional Biases." Working Paper, June 2023.
- 20 Dec 2017
- Lessons from the Classroom
How to Design a Better Customer Experience
Dietz, who didn’t realize that children saw the company’s MRI scanners as cold, scary chambers of misery until he visited a hospital and saw for himself. The machines created enough fear View Details
- Research Summary
Overview
Engaged with field work in East Africa, South Asia, and in several large hybrid organizations in the United States, Professor Whillans places a focus on exploring questions with strong theoretical motivation in the social psychological literature and relevant... View Details
- 24 Jan 2005
- Research & Ideas
Rethinking Activity-Based Costing
machine is available to work 40 hours per week, its practical full capacity is 32 to 35 hours per week. Typically, managers would allot a lower rate—say 80 percent—to people, allowing 20 percent of their time for breaks, arrival View Details
Keywords: by Robert S. Kaplan & Steven R. Anderson
- 2022
- Article
Efficiently Training Low-Curvature Neural Networks
By: Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju and Francois Fleuret
Standard deep neural networks often have excess non-linearity, making them susceptible to issues such as low adversarial robustness and gradient instability. Common methods to address these downstream issues, such as adversarial training, are expensive and often... View Details
Keywords: AI and Machine Learning
Srinivas, Suraj, Kyle Matoba, Himabindu Lakkaraju, and Francois Fleuret. "Efficiently Training Low-Curvature Neural Networks." Advances in Neural Information Processing Systems (NeurIPS) (2022).
- 06 Jul 2020
- Research & Ideas
The Right Way to Manage Customer Churn for Maximum Profit
the example of an online retailer who suddenly sees a monthly shopper stop buying for two months. “Customers just stop using the service but don’t have to tell the company.” In order to manage churn, companies typically use machine View Details
- 2024
- Conference Paper
Quantifying Uncertainty in Natural Language Explanations of Large Language Models
By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal
Large Language Models (LLMs) are increasingly used as powerful tools for several
high-stakes natural language processing (NLP) applications. Recent prompting
works claim to elicit intermediate reasoning steps and key tokens that serve as
proxy explanations for LLM... View Details
Lakkaraju, Himabindu, Sree Harsha Tanneru, and Chirag Agarwal. "Quantifying Uncertainty in Natural Language Explanations of Large Language Models." Paper presented at the Society for Artificial Intelligence and Statistics, 2024.
- 09 Dec 2019
- Research & Ideas
Identify Great Customers from Their First Purchase
Using information collected during a customer’s first purchase, a new marketing tool that leverages machine learning technology can provide firms with valuable predictions about the customer’s future... View Details
- April 2023
- Article
On the Privacy Risks of Algorithmic Recourse
By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected... View Details
Pawelczyk, Martin, Himabindu Lakkaraju, and Seth Neel. "On the Privacy Risks of Algorithmic Recourse." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 206 (April 2023).
- November–December 2024
- Article
Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing
By: Kirk Bansak and Elisabeth Paulson
This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a multi-year pilot in Switzerland, seeks to maximize the average predicted employment... View Details
Bansak, Kirk, and Elisabeth Paulson. "Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing." Operations Research 72, no. 6 (November–December 2024): 2375–2390.
- 2023
- Working Paper
Distributionally Robust Causal Inference with Observational Data
By: Dimitris Bertsimas, Kosuke Imai and Michael Lingzhi Li
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders. Our approach is based on distributionally robust optimization and proceeds in two steps. We first... View Details
Bertsimas, Dimitris, Kosuke Imai, and Michael Lingzhi Li. "Distributionally Robust Causal Inference with Observational Data." Working Paper, February 2023.