Filter Results:
(97)
Show Results For
- All HBS Web
(492)
- Faculty Publications (97)
Show Results For
- All HBS Web
(492)
- Faculty Publications (97)
- 2024
- Working Paper
The Uneven Impact of Generative AI on Entrepreneurial Performance
By: Nicholas G. Otis, Rowan Clarke, Solène Delecourt, David Holtz and Rembrand Koning
Scalable and low-cost AI assistance has the potential to improve firm decision-making and economic performance. However, running a business involves a myriad of open-ended problems, making it difficult to know whether recent AI advances can help business owners make... View Details
Keywords: AI and Machine Learning; Performance Improvement; Small Business; Decision Choices and Conditions; Kenya
Otis, Nicholas G., Rowan Clarke, Solène Delecourt, David Holtz, and Rembrand Koning. "The Uneven Impact of Generative AI on Entrepreneurial Performance." Harvard Business School Working Paper, No. 24-042, December 2023.
- 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).
- November 2023
- Case
Open Source Machine Learning at Google
Set in early 2023, the case exposes students to the challenges of managing open source software at Google. The case focuses on the challenges for Alex Spinelli, Vice President of Product Management for Core Machine Learning. He must set priorities for Google’s efforts... View Details
Keywords: Decision Choices and Conditions; Technological Innovation; Open Source Distribution; Strategy; AI and Machine Learning; Applications and Software; Technology Industry; United States
Greenstein, Shane, Martin Wattenberg, Fernanda B. Viégas, Daniel Yue, and James Barnett. "Open Source Machine Learning at Google." Harvard Business School Case 624-015, November 2023.
- November 2023
- Case
Copilot(s): Generative AI at Microsoft and GitHub
This case tells the story of Microsoft’s 2018 acquisition of GitHub and the subsequent launch of GitHub Copilot, a tool that uses generative artificial intelligence to suggest snippets of code to software developers in real time. Set in late 2021, when Copilot was... View Details
Keywords: Business Ventures; Strategy; AI and Machine Learning; Applications and Software; Product Launch; Information Technology Industry; Technology Industry; Web Services Industry; United States; California
Nagle, Frank, Shane Greenstein, Maria P. Roche, Nataliya Langburd Wright, and Sarah Mehta. "Copilot(s): Generative AI at Microsoft and GitHub." Harvard Business School Case 624-010, November 2023.
- November 2023 (Revised April 2024)
- Case
Khanmigo: Revolutionizing Learning with GenAI
By: William A. Sahlman, Allison M. Ciechanover and Emily Grandjean
Already a leader in the edtech space since its 2008 launch, Khan Academy was now one of the first edtech organizations to embrace generative artificial intelligence ("genAI"). In March 2023, Khan Academy began beta testing Khanmigo, a genAI “guide” and tutor built with... View Details
Keywords: Technology Adoption; Leading Change; Entrepreneurship; Risk and Uncertainty; Education; AI and Machine Learning; Corporate Social Responsibility and Impact; Education Industry; Technology Industry; United States; San Francisco
Sahlman, William A., Allison M. Ciechanover, and Emily Grandjean. "Khanmigo: Revolutionizing Learning with GenAI." Harvard Business School Case 824-059, November 2023. (Revised April 2024.)
- November–December 2023
- Article
Keep Your AI Projects on Track
By: Iavor Bojinov
AI—and especially its newest star, generative AI—is today a central theme in corporate boardrooms, leadership discussions, and casual exchanges among employees eager to supercharge their productivity. Sadly, beneath the aspirational headlines and tantalizing potential... View Details
Keywords: Generative Models; AI and Machine Learning; Success; Failure; Product Development; Technology Adoption
Bojinov, Iavor. "Keep Your AI Projects on Track." Harvard Business Review 101, no. 6 (November–December 2023): 53–59.
- October 2023 (Revised February 2024)
- Case
Loris
By: Shunyuan Zhang, Das Narayandas, Stacy Straaberg and David Lane
In December 2022, Loris’s executive team considered their go-to-market strategy. Loris was an artificial intelligence (AI) software startup for the customer service industry with two products on the market: 1) Agent Assist which provided customer service agents (CSAs)... View Details
- 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.
- September 2023 (Revised January 2024)
- Case
AI21 Labs in 2023: Strategy for Generative AI
By: David Yoffie, Orna Dan and Elena Corsi
Israeli generative artificial intelligence company AI21 Labs was founded in 2017 to realize the vision of true machine intelligence. It sought to reinvent writing and reading and in 2020 it launched Wordtune, an app using GenAI software to offer alternate text... View Details
Keywords: Decision Making; AI and Machine Learning; Innovation Strategy; Growth and Development Strategy; Applications and Software; Competitive Strategy; Technology Industry; Israel
Yoffie, David, Orna Dan, and Elena Corsi. "AI21 Labs in 2023: Strategy for Generative AI." Harvard Business School Case 724-383, September 2023. (Revised January 2024.)
- 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.
- August 2023
- Case
Kariyer.net: Recruiting AI
By: Shunyuan Zhang, Fares Khrais and Namrata Arora
In 2017, Fatih Uysal (AMP 2021) became CEO of Kariyer.net. By then, the business was already the industry leading online job board in Turkey. However, faced with stalling growth, a turbulent macroenvironment, and growing competition from international players, Uysal... View Details
Keywords: Online Technology; Marketing; Websites; Artificial Intelligence; Innovation; Two-sided Platforms; Internet and the Web; Product Launch; Product Positioning; Job Search; Employment; Transformation; Volatility; Innovation and Invention; Disruptive Innovation; Management Practices and Processes; Business Growth and Maturation; Competitive Strategy; Business Startups; Talent and Talent Management; Cost vs Benefits; Macroeconomics; Corporate Entrepreneurship; Emerging Markets; Digital Platforms; Employment Industry; Information Technology Industry; Technology Industry; Middle East; Turkey
Zhang, Shunyuan, Fares Khrais, and Namrata Arora. "Kariyer.net: Recruiting AI." Harvard Business School Case 524-014, August 2023.
- 2024
- Working Paper
The Crowdless Future? Generative AI and Creative Problem Solving
The rapid advances in generative artificial intelligence (AI) open up attractive opportunities for creative problem-solving through human-guided AI partnerships. To explore this potential, we initiated a crowdsourcing challenge focused on sustainable, circular economy... View Details
Keywords: Large Language Models; Crowdsourcing; Generative Ai; Creative Problem-solving; Organizational Search; AI-in-the-loop; Prompt Engineering; AI and Machine Learning; Innovation and Invention
Boussioux, Léonard, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic, and Karim R. Lakhani. "The Crowdless Future? Generative AI and Creative Problem Solving." Harvard Business School Working Paper, No. 24-005, July 2023. (Revised July 2024.)
- August 2023
- Article
Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel
By: Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju and Sameer Singh
Practitioners increasingly use machine learning (ML) models, yet models have become more complex and harder to understand. To understand complex models, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use... View Details
Slack, Dylan, Satyapriya Krishna, Himabindu Lakkaraju, and Sameer Singh. "Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel." Nature Machine Intelligence 5, no. 8 (August 2023): 873–883.
- 2023
- Article
Towards Bridging the Gaps between the Right to Explanation and the Right to Be Forgotten
By: Himabindu Lakkaraju, Satyapriya Krishna and Jiaqi Ma
The Right to Explanation and the Right to be Forgotten are two important principles outlined to regulate algorithmic decision making and data usage in real-world applications. While the right to explanation allows individuals to request an actionable explanation for an... View Details
Keywords: Analytics and Data Science; AI and Machine Learning; Decision Making; Governing Rules, Regulations, and Reforms
Lakkaraju, Himabindu, Satyapriya Krishna, and Jiaqi Ma. "Towards Bridging the Gaps between the Right to Explanation and the Right to Be Forgotten." Proceedings of the International Conference on Machine Learning (ICML) 40th (2023): 17808–17826.
- July 2023
- Supplement
Honeycomb (B): Jumping on The Generative AI Bandwagon?
By: Jeffrey J. Bussgang and Kumba Sennaar
Honeycomb, an audio app enabling users to record stories and save family memories, considers pivoting to embrace generative AI. What should the co-founders business model look like if they pursued this new direction? View Details
- July 2023 (Revised July 2023)
- Background Note
Generative AI Value Chain
By: Andy Wu and Matt Higgins
Generative AI refers to a type of artificial intelligence (AI) that can create new content (e.g., text, image, or audio) in response to a prompt from a user. ChatGPT, Bard, and Claude are examples of text generating AIs, and DALL-E, Midjourney, and Stable Diffusion are... View Details
Keywords: AI; Artificial Intelligence; Model; Hardware; Data Centers; AI and Machine Learning; Applications and Software; Analytics and Data Science; Value
Wu, Andy, and Matt Higgins. "Generative AI Value Chain." Harvard Business School Background Note 724-355, July 2023. (Revised July 2023.)
- June 2023 (Revised July 2023)
- Case
Social Media Background Screening at Fama Technologies
By: Joseph Pacelli, Jillian Grennan and Alexis Lefort
Fama Technologies is an online screening company that uses AI to analyze job applicants' publicly available online content for signs of risk and culture fit. The case opens with Ben Mones, founder and CEO, looking to secure funding from venture firms. He is running... View Details
Keywords: Human Resources; Recruitment; Retention; Selection and Staffing; Organizational Culture; Talent and Talent Management; AI and Machine Learning; Social Media; Venture Capital; Entrepreneurship; United States
Pacelli, Joseph, Jillian Grennan, and Alexis Lefort. "Social Media Background Screening at Fama Technologies." Harvard Business School Case 123-010, June 2023. (Revised July 2023.)
- June 2023
- Supplement
Social Media Background Screening at Fama Technologies (B)
By: Joseph Pacelli, Jillian Grennan and Alexis Lefort
Fama Technologies is an online screening company that uses AI to analyze job applicants' publicly available online content for signs of risk and culture fit. The case opens with Ben Mones, founder and CEO, looking to secure funding from venture firms. He is running... View Details
Keywords: Human Resources; Recruitment; Retention; Selection and Staffing; Organizational Culture; Talent and Talent Management; United States
Pacelli, Joseph, Jillian Grennan, and Alexis Lefort. "Social Media Background Screening at Fama Technologies (B)." Harvard Business School Supplement 123-086, June 2023.
- June 19, 2023
- Article
Should You Start a Generative AI Company?
Many entrepreneurs are considering starting companies that leverage the latest generative AI technology, but they must ask themselves whether they have what it takes to compete on increasingly commoditized foundational models, or whether they should instead... View Details
De Freitas, Julian. "Should You Start a Generative AI Company?" Harvard Business Review (website) (June 19, 2023).
- 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.