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(889)
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- Faculty Publications (467)
Show Results For
- All HBS Web
(889)
- People (1)
- News (155)
- Research (565)
- Events (10)
- Multimedia (3)
- Faculty Publications (467)
- 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
Neeley, Tsedal. "8 Questions About Using AI Responsibly, Answered." Harvard Business Review (website) (May 9, 2023).
- 2024
- Working Paper
Lessons from an App Update at Replika AI: Identity Discontinuity in Human-AI Relationships
By: Julian De Freitas, Noah Castelo, Ahmet Uğuralp and Zeliha Uğuralp
Can consumers form deep emotional bonds with AI and be vested in AI identities over time? We
leverage a natural app-update event at Replika AI, a popular US-based AI companion, to shed
light on these questions. We find that customers feel closer to their AI companion... View Details
De Freitas, Julian, Noah Castelo, Ahmet Uğuralp, and Zeliha Uğuralp. "Lessons from an App Update at Replika AI: Identity Discontinuity in Human-AI Relationships." Harvard Business School Working Paper, No. 25-018, October 2024.
- 2024
- Working Paper
The Wade Test: Generative AI and CEO Communication
By: Prithwiraj Choudhury, Bart S. Vanneste and Amirhossein Zohrehvand
Can generative artificial intelligence (AI) transform the role of the CEO by effectively automating CEO
communication? This study investigates whether AI can mimic a human CEO and whether employees’
perception of the communication’s source matter. In a field... View Details
Choudhury, Prithwiraj, Bart S. Vanneste, and Amirhossein Zohrehvand. "The Wade Test: Generative AI and CEO Communication." Harvard Business School Working Paper, No. 25-008, August 2024.
- 2023
- Working Paper
Black-box Training Data Identification in GANs via Detector Networks
By: Lukman Olagoke, Salil Vadhan and Seth Neel
Since their inception Generative Adversarial Networks (GANs) have been popular generative models across images, audio, video, and tabular data. In this paper we study whether given access to a trained GAN, as well as fresh samples from the underlying distribution, if... View Details
Olagoke, Lukman, Salil Vadhan, and Seth Neel. "Black-box Training Data Identification in GANs via Detector Networks." Working Paper, October 2023.
- May–June 2024
- Article
Should Your Brand Hire a Virtual Influencer?
By: Serim Hwang, Shunyuan Zhang, Xiao Liu and Kannan Srinivasan
Followers respond more favorably to sponsored posts by virtual influencers versus those by humans, costs are lower, and creating an influencer from scratch allows marketers to introduce more diversity. View Details
Hwang, Serim, Shunyuan Zhang, Xiao Liu, and Kannan Srinivasan. "Should Your Brand Hire a Virtual Influencer?" Harvard Business Review 102, no. 3 (May–June 2024): 56–60.
- October 31, 2022
- Article
Achieving Individual—and Organizational—Value with AI
By: Sam Ransbotham, David Kiron, François Candelon, Shervin Khodabandeh and Michael Chu
New research shows that employees derive individual value from AI when using the technology improves their sense of competency, autonomy, and relatedness. Likewise, organizations are far more likely to obtain value from AI when their workers do. This report offers key... View Details
Ransbotham, Sam, David Kiron, François Candelon, Shervin Khodabandeh, and Michael Chu. "Achieving Individual—and Organizational—Value with AI." MIT Sloan Management Review, Big Ideas Artificial Intelligence and Business Strategy Initiative (website) (October 31, 2022). (Findings from the 2022 Artificial Intelligence and Business Strategy Global Executive Study and Research Project.)
- September 2024
- Exercise
Building an AI First Snack Company: A Hands-on Generative AI Exercise
By: Iavor I. Bojinov
Although the term 'Generative AI' (GenAI) is widely recognized, its practical application in daily workflows has yet to be understood. This exercise introduces students to GenAI tools, demonstrating how they can be seamlessly integrated into professional work practices... View Details
Keywords: AI and Machine Learning; Technology Adoption; Marketing Strategy; Product Launch; Brands and Branding
Bojinov, Iavor I. "Building an AI First Snack Company: A Hands-on Generative AI Exercise." Harvard Business School Exercise 625-052, September 2024.
- March 2023
- Teaching Note
VideaHealth: Building the AI Factory
By: Karim R. Lakhani
Teaching Note for HBS Case No. 621-021. The case “VideaHealth: Building the AI Factory” examines the creation of dental startup VideaHealth (Videa) and the development of its artificial intelligence (AI)-led business strategy through the eyes of founder and CEO Florian... View Details
- 01 Dec 2020
- News
AI Enhances Diagnostic Care
changing the field of medicine. In the past several decades, the growing use of artificial intelligence in the health care sector has made it possible for computer systems and diagnostic machines to learn... View Details
Keywords: Jennifer Gillespie
- January 2024 (Revised February 2024)
- Case
OpenAI: Idealism Meets Capitalism
By: Shikhar Ghosh and Shweta Bagai
In November 2023, the board of OpenAI, one of the most successful companies in the history of technology, decided to fire Sam Altman, its charismatic and influential CEO. Their decision shocked the corporate world and had people wondering why OpenAI had designed a... View Details
Keywords: AI; AI and Machine Learning; Governing and Advisory Boards; Ethics; Strategy; Technological Innovation; Leadership
Ghosh, Shikhar, and Shweta Bagai. "OpenAI: Idealism Meets Capitalism." Harvard Business School Case 824-134, January 2024. (Revised February 2024.)
- 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.
- 23 Oct 2019
- News
Preparing Leaders to Leverage Artificial Intelligence
learning techniques to draw conclusions that weren’t possible with traditional methods, and creating new machine learning approaches and algorithms. They have published more... View Details
- 29 Sep 2020
- Blog Post
New Life for Old Tech: Startup Provides Network Security Solutions for Obsolete Devices
device-specific representation of each device on the network, and our generalizable machine learning approach and software-based installation allows us to scale to all legacy and modern devices.” For Breen,... View Details
- 16 Nov 2020
- Blog Post
Flatiron School: Reflections from Summer 2020
Birchbox, Young Invincibles, Color Camp, Women 2.0 and Casper. WHAT WERE YOUR GOALS FOR THE SUMMER? Rocio Wu (MBA 2020): Learning python and machine learning had always been on... View Details
Keywords: All Industries
- January–February 2023
- Article
Forecasting COVID-19 and Analyzing the Effect of Government Interventions
By: Michael Lingzhi Li, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis and Dimitris Bertsimas
We developed DELPHI, a novel epidemiological model for predicting detected cases and deaths in the prevaccination era of the COVID-19 pandemic. The model allows for underdetection of infections and effects of government interventions. We have applied DELPHI across more... View Details
Keywords: COVID-19 Pandemic; Epidemics; Analytics and Data Science; Health Pandemics; AI and Machine Learning; Forecasting and Prediction
Li, Michael Lingzhi, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis, and Dimitris Bertsimas. "Forecasting COVID-19 and Analyzing the Effect of Government Interventions." Operations Research 71, no. 1 (January–February 2023): 184–201.
- Student-Profile
Ta-Wei "David" Huang
management and using causal inference / machine learning tools to solve marketing problems.” It was this motivation that led him to pursue a Ph.D. Initially, David’s familiarity with HBS was limited to the... View Details
- 2024
- Working Paper
The Narrative AI Advantage? A Field Experiment on Generative AI-Augmented Evaluations of Early-Stage Innovations
By: Jacqueline N. Lane, Léonard Boussioux, Charles Ayoubi, Ying Hao Chen, Camila Lin, Rebecca Spens, Pooja Wagh and Pei-Hsin Wang
The rise of generative artificial intelligence (AI) is transforming creative problem-solving, necessitating new approaches for evaluating innovative solutions. This study explores how human-AI collaboration can enhance early-stage evaluations, focusing on the interplay... View Details
Lane, Jacqueline N., Léonard Boussioux, Charles Ayoubi, Ying Hao Chen, Camila Lin, Rebecca Spens, Pooja Wagh, and Pei-Hsin Wang. "The Narrative AI Advantage? A Field Experiment on Generative AI-Augmented Evaluations of Early-Stage Innovations." Harvard Business School Working Paper, No. 25-001, August 2024. (Revised August 2024.)
- April 2024 (Revised May 2024)
- Case
Anthropic: Building Safe AI
By: Shikhar Ghosh and Shweta Bagai
In March 2024, Anthropic, a leading AI safety and research company, made headlines with the launch of Claude 3, its most advanced AI model. This marked Anthropic’s bold entry into the multimodal GenAI domain, showcasing capabilities extending to both image and text... 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 May 2024.)
- Student-Profile
Mengjie "Magie" Cheng
Magie Cheng (she/her) worked for a social network company in their machine learning group and spent much of her time analyzing user behavior for a wide range of social networking applications. She became... View Details
- 04 Sep 2019
- News
Advancing Diagnostics that Can Save Lives
the lab. In 2016 Lee and four Harvard cofounders launched DZD to apply genome sequencing and machine learning to the problem of growing antibiotic resistance. Their technology helps doctors diagnose... View Details
Keywords: Susan Young