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Show Results For
- All HBS Web
(1,432)
- People (1)
- News (271)
- Research (903)
- Events (17)
- Multimedia (6)
- Faculty Publications (724)
- May 2016 (Revised April 2018)
- Case
Building the Digital Manufacturing Enterprise of the Future at Siemens
By: Willy Shih
This case describes the motivation for and the development of Siemens' digital manufacturing enterprise vision, which became the foundation for its implementation of Industrie 4.0. While the effort started with a purely defensive move by Anton Huber, head of the... View Details
Keywords: Big Data; Internet Of Things; Internet Of Everything; Industrie 4.0; Digital Factory; Digital Enterprise; Digital Manufacturing; Manufacturing; Production Management; Production Planning; Computer Software; Germany; German Manufacturing; Machinery and Machining; Information Technology; Digital Platforms; Technological Innovation; Production; Supply Chain; Applications and Software; Information Infrastructure; Internet and the Web; Analytics and Data Science; Manufacturing Industry; Germany
Shih, Willy. "Building the Digital Manufacturing Enterprise of the Future at Siemens." Harvard Business School Case 616-060, May 2016. (Revised April 2018.)
- 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.
- 2023
- Article
Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators
By: Benjamin Jakubowski, Siram Somanchi, Edward McFowland III and Daniel B. Neill
Regression discontinuity (RD) designs are widely used to estimate causal effects in the absence of a randomized experiment. However, standard approaches to RD analysis face two significant limitations. First, they require a priori knowledge of discontinuities in... View Details
Jakubowski, Benjamin, Siram Somanchi, Edward McFowland III, and Daniel B. Neill. "Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators." Journal of Machine Learning Research 24, no. 133 (2023): 1–57.
- January–February 2025
- Article
Why People Resist Embracing AI
The success of AI depends not only on its capabilities, which are becoming more advanced each day, but on people’s willingness to harness them. Unfortunately, many people view AI negatively, fearing it will cause job losses, increase the likelihood that their personal... View Details
De Freitas, Julian. "Why People Resist Embracing AI." Harvard Business Review 103, no. 1 (January–February 2025): 52–56.
- Winter 2021
- Editorial
Introduction
This issue of Negotiation Journal is dedicated to the theme of artificial intelligence, technology, and negotiation. It arose from a Program on Negotiation (PON) working conference on that important topic held virtually on May 17–18. The conference was not the... View Details
Wheeler, Michael A. "Introduction." Special Issue on Artificial Intelligence, Technology, and Negotiation. Negotiation Journal 37, no. 1 (Winter 2021): 5–12.
- Teaching Interest
Overview
By: V.G. Narayanan
I teach accounting to MBA students, executives, and Harvard Extension School students. I teach topics from both financial and managerial accounting. I also train professors in teaching by the case method. View Details
- March 16, 2021
- Article
From Driverless Dilemmas to More Practical Commonsense Tests for Automated Vehicles
By: Julian De Freitas, Andrea Censi, Bryant Walker Smith, Luigi Di Lillo, Sam E. Anthony and Emilio Frazzoli
For the first time in history, automated vehicles (AVs) are being deployed in populated environments. This unprecedented transformation of our everyday lives demands a significant undertaking: endowing
complex autonomous systems with ethically acceptable behavior. We... View Details
Keywords: Automated Driving; Public Health; Artificial Intelligence; Transportation; Health; Ethics; Policy; AI and Machine Learning
De Freitas, Julian, Andrea Censi, Bryant Walker Smith, Luigi Di Lillo, Sam E. Anthony, and Emilio Frazzoli. "From Driverless Dilemmas to More Practical Commonsense Tests for Automated Vehicles." Proceedings of the National Academy of Sciences 118, no. 11 (March 16, 2021).
- 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.
- January 2025
- Case
Summer Health: Raising an AI-First Company?
By: Jeffrey J. Bussgang, Sarah Mehta and Maxim Pike Harrell
In October 2023, Summer Health CEO Ellen DaSilva arrived at a defining juncture for her pediatric telehealth startup. Founded in 2021, Summer Health offered parents rapid access to licensed pediatricians via text message. DaSilva, an experienced telehealth executive,... View Details
Keywords: AI and Machine Learning; Technology Adoption; Entrepreneurship; Leadership; Health Industry; Telecommunications Industry; United States
Bussgang, Jeffrey J., Sarah Mehta, and Maxim Pike Harrell. "Summer Health: Raising an AI-First Company?" Harvard Business School Case 825-083, January 2025.
- 2023
- Working Paper
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits
By: Biyonka Liang and Iavor I. Bojinov
Typically, multi-armed bandit (MAB) experiments are analyzed at the end of the study and thus require the analyst to specify a fixed sample size in advance. However, in many online learning applications, it is advantageous to continuously produce inference on the... View Details
Liang, Biyonka, and Iavor I. Bojinov. "An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits." Harvard Business School Working Paper, No. 24-057, March 2024.
- July 2024
- Case
Replika AI: Alleviating Loneliness (A)
By: Shikhar Ghosh and Shweta Bagai
Eugenia Kuyda launched Replika AI in 2017 as an empathetic digital companion to combat loneliness and provide emotional support. The platform surged in popularity during the COVID-19 pandemic, offering non-judgmental support to isolated users. By 2023, Replika boasted... View Details
Keywords: Entrepreneurship; Ethics; Health Pandemics; AI and Machine Learning; Well-being; Technology Industry
Ghosh, Shikhar, and Shweta Bagai. "Replika AI: Alleviating Loneliness (A)." Harvard Business School Case 824-088, July 2024.
- 17 Feb 2016
- Research & Ideas
Man vs. Machine: Which Makes Better Hires?
popular in the 1950s and ’60s as a way of sifting through bulging applicant pools. After researchers questioned its reliability, testing fell out of use in favor of personal interviews. Now, with the emergence of big data, View Details
- April 2024 (Revised December 2024)
- Case
Anthropic: Building Safe AI
By: Shikhar Ghosh and Shweta Bagai
In late 2024, Anthropic, a leading AI safety and research company, achieved a significant breakthrough with computer use capabilities that allowed AI to interact with computers like humans. Co-founded by former OpenAI employees and known for its generative AI... View Details
Keywords: AI and Machine Learning; Corporate Accountability; Corporate Social Responsibility and Impact; Business Growth and Maturation; Corporate Strategy; Technology Industry; United States
Ghosh, Shikhar, and Shweta Bagai. "Anthropic: Building Safe AI." Harvard Business School Case 824-129, April 2024. (Revised December 2024.)
- 2025
- Working Paper
The Impact of Input Inaccuracy on Leveraging AI Tools: Evidence from Algorithmic Labor Scheduling
By: Caleb Kwon, Antonio Moreno and Ananth Raman
Are the inputs used by your AI tool correct and up to date? In this paper, we show that the answer to this question: (i) is frequently a “no” in real business contexts, and (ii) has significant implications on the performance of AI tools. In the context of algorithmic... View Details
Kwon, Caleb, Antonio Moreno, and Ananth Raman. "The Impact of Input Inaccuracy on Leveraging AI Tools: Evidence from Algorithmic Labor Scheduling." Working Paper, 2025.
- 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.
- October 2019
- Case
Feeling Machines: Emotion AI at Affectiva
By: Shane Greenstein and John Masko
In 2016, Affectiva—a Boston-based emotion AI software company with a long track record of building emotion-sensing software for market research—had attempted to expand into new verticals by releasing a mobile software development kit (SDK) that downloaders could adapt... View Details
Keywords: Artificial Intelligence; Market Research; Business Model; Finance; Revenue; Decision Making; Risk and Uncertainty; Market Entry and Exit; Applications and Software; AI and Machine Learning; Information Technology Industry; Auto Industry; United States
Greenstein, Shane, and John Masko. "Feeling Machines: Emotion AI at Affectiva." Harvard Business School Case 620-058, October 2019.
- Career Coach
Yiwei Zhao
company, where she gained insights into machine learning development cycles. She can help students understand how tech companies of different stages recruit, what it’s like to build a career in tech, and how... View Details
- 2023
- Article
On the Impact of Actionable Explanations on Social Segregation
By: Ruijiang Gao and Himabindu Lakkaraju
As predictive models seep into several real-world applications, it has become critical to ensure that individuals who are negatively impacted by the outcomes of these models are provided with a means for recourse. To this end, there has been a growing body of research... View Details
Gao, Ruijiang, and Himabindu Lakkaraju. "On the Impact of Actionable Explanations on Social Segregation." Proceedings of the International Conference on Machine Learning (ICML) 40th (2023): 10727–10743.
- Article
Fake AI People Won't Fix Online Dating
Computer-generated images may inspire even more distrust and surely won’t lead to the love of a lifetime. View Details
Keywords: Artificial Intelligence; Dating Services; Internet and the Web; Ethics; AI and Machine Learning
Kominers, Scott Duke. "Fake AI People Won't Fix Online Dating." Bloomberg Opinion (January 16, 2020).
- July 2024 (Revised December 2024)
- Case
Compass Ethics: Governing Through Ethical Principles at WeCorp Industries
By: Elisabeth Kempf and Jesse M. Shapiro
Andrew Hill is Chief Information Officer at WeCorp, a UK-based defense technology startup specializing in drone technology. WeCorp faces decisions about international licensing and AI integration in its drones. In partnership with Compass Ethics, Hill aims to establish... View Details
Keywords: Business Startups; Decision Making; Ethics; Entrepreneurial Finance; AI and Machine Learning; Technology Industry; United Kingdom
Kempf, Elisabeth, and Jesse M. Shapiro. "Compass Ethics: Governing Through Ethical Principles at WeCorp Industries." Harvard Business School Case 224-105, July 2024. (Revised December 2024.)