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- 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.
- Article
Use of Crowd Innovation to Develop an Artificial Intelligence-Based Solution for Radiation Therapy Targeting
By: Raymond H. Mak, Michael G. Endres, Jin Hyun Paik, Rinat A. Sergeev, Hugo Aerts, Christopher L. Williams, Karim R. Lakhani and Eva C. Guinan
Importance: Radiation therapy (RT) is a critical cancer treatment, but the existing radiation oncologist work force does not meet growing global demand. One key physician task in RT planning involves tumor segmentation for targeting, which requires substantial... View Details
Keywords: Crowdsourcing; AI Algorithms; Health Care and Treatment; Collaborative Innovation and Invention; AI and Machine Learning
Mak, Raymond H., Michael G. Endres, Jin Hyun Paik, Rinat A. Sergeev, Hugo Aerts, Christopher L. Williams, Karim R. Lakhani, and Eva C. Guinan. "Use of Crowd Innovation to Develop an Artificial Intelligence-Based Solution for Radiation Therapy Targeting." JAMA Oncology 5, no. 5 (May 2019): 654–661.
- December 2018 (Revised March 2021)
- Background Note
Modern Automation (A): Artificial Intelligence
By: William R. Kerr and James Palano
This primer is meant to be a field guide to the late 2010s' surge in business use of "Artificial Intelligence" (AI), or enterprise software based in machine learning. First, it provides an overview of the key trends—digitization, connectivity, the continuation of... View Details
Keywords: Artificial Intelligence; Digitization; Connectivity; Computing; Future Of Work; Automation; AI and Machine Learning
Kerr, William R., and James Palano. "Modern Automation (A): Artificial Intelligence." Harvard Business School Background Note 819-084, December 2018. (Revised March 2021.)
- December 2018
- Teaching Note
Autonomous Vehicles: The Rubber Hits the Road…but When?
By: William Kerr and James Palano
The autonomous vehicles have enormous implications for business and society. But, despite the headline-laden attention paid to the technology, there remain more questions than answers. Students will learn about the complex industry and have explicit discussions about... View Details
- August 2018 (Revised October 2020)
- Case
Tailor Brands: Artificial Intelligence-Driven Branding
By: Jill Avery
Using proprietary artificial intelligence technology, startup Tailor Brands set out to democratize branding by allowing small businesses to create their brand identities by automatically generating logos in just minutes at minimal cost with no branding or design skills... View Details
Keywords: Startup; Services; Artificial Intelligence; Machine Learning; Digital Marketing; Brand Management; Big Data; Internet Marketing; Analytics; Marketing; Marketing Strategy; Brands and Branding; Information Technology; Entrepreneurship; Venture Capital; Business Model; Consumer Behavior; AI and Machine Learning; Analytics and Data Science; Advertising Industry; Service Industry; Technology Industry; United States; North America; Israel
Avery, Jill. "Tailor Brands: Artificial Intelligence-Driven Branding." Harvard Business School Case 519-017, August 2018. (Revised October 2020.)
- May 2018 (Revised February 2019)
- Case
The Powers That Be (Internet Edition): Google, Apple, Facebook, Amazon, and Microsoft
By: Jeffrey F. Rayport, Julia Kelley and Nathaniel Schwalb
As of early 2018, five U.S. technology companies—Google, Apple, Facebook, Amazon, and Microsoft—were among the largest companies in the world. Similarly, three Chinese technology firms—Baidu, Alibaba, and Tencent, or BAT—had emerged as global players due in part to the... View Details
Keywords: Internet and the Web; Business Ventures; Customers; Analytics and Data Science; Safety; Corporate Strategy; Competitive Strategy; Technology Industry
Rayport, Jeffrey F., Julia Kelley, and Nathaniel Schwalb. "The Powers That Be (Internet Edition): Google, Apple, Facebook, Amazon, and Microsoft." Harvard Business School Case 818-111, May 2018. (Revised February 2019.)
- January 2018 (Revised March 2019)
- Case
Autonomous Vehicles: The Rubber Hits the Road...but When?
By: William Kerr, Allison Ciechanover, Jeff Huizinga and James Palano
The rise of autonomous vehicles has enormous implications for business and society. Despite the many headlines and significant investment in the technology by early 2019, it was still unclear when truly autonomous vehicles would be a commercial reality. Students will... View Details
Keywords: Technology Management; Artificial Intelligence; General Management; Robotics; Technological Innovation; Transportation; Disruption; Information Technology; Decision Making; AI and Machine Learning; Auto Industry; Technology Industry
Kerr, William, Allison Ciechanover, Jeff Huizinga, and James Palano. "Autonomous Vehicles: The Rubber Hits the Road...but When?" Harvard Business School Case 818-088, January 2018. (Revised March 2019.)
- August 2017 (Revised July 2019)
- Case
GROW: Using Artificial Intelligence to Screen Human Intelligence
By: Ethan Bernstein, Paul McKinnon and Paul Yarabe
Over 10% of all 2017 university graduates in Japan used GROW, an artificial intelligence platform and mobile app developed by Tokyo-based people analytics startup IGS, to recruit for a job. This case puts participants in the shoes of IGS founder and CEO Masahiro... View Details
Keywords: Big Data; Artificial Intelligence; Talent and Talent Management; Recruitment; Selection and Staffing; Human Resources; Information Technology; AI and Machine Learning; Analytics and Data Science; Financial Services Industry; Air Transportation Industry; Advertising Industry; Manufacturing Industry; Technology Industry; Japan
Bernstein, Ethan, Paul McKinnon, and Paul Yarabe. "GROW: Using Artificial Intelligence to Screen Human Intelligence." Harvard Business School Case 418-020, August 2017. (Revised July 2019.)
- June 2016 (Revised August 2019)
- Case
Numenta: Inventing and (or) Commercializing AI
By: David B. Yoffie, Liz Kind and David Ben Shimol
In March 2016, Donna Dubinsky (co-founder and CEO) and Jeff Hawkins (co-founder) were struggling with a key question: Could Numenta be successful in both creating fundamental technology and building a commercial business? Located in Redwood City, CA, Numenta was... View Details
Keywords: Artificial Intelligence; Machine Intelligence; Machine Learning; Strategy; Business Model; Entrepreneurship; Information; Technological Innovation; Research; Research and Development; Information Technology; Applications and Software; Technology Adoption; Digital Platforms; Commercialization; AI and Machine Learning
Yoffie, David B., Liz Kind, and David Ben Shimol. "Numenta: Inventing and (or) Commercializing AI." Harvard Business School Case 716-469, June 2016. (Revised August 2019.)
- Forthcoming
- Article
Disclosure, Humanizing, and Contextual Vulnerability of Generative AI Chatbots
By: Julian De Freitas and I. Glenn Cohen
In the wake of recent advancements in generative AI, regulatory bodies are trying to keep pace. One key decision is whether to require app makers to disclose the use of generative AI-powered chatbots in their products. We suggest that some generative AI-based chatbots... View Details
Keywords: AI and Machine Learning; Governing Rules, Regulations, and Reforms; Applications and Software; Well-being
De Freitas, Julian, and I. Glenn Cohen. "Disclosure, Humanizing, and Contextual Vulnerability of Generative AI Chatbots." New England Journal of Medicine AI (forthcoming).
- Teaching Interest
Harvard Business Analytics Program: Operations and Supply Chain Management
By: Dennis Campbell
Digital technologies and data analytics are radically changing the operating model of an organization and how it connects to its broader supply chain and ecosystem. This course emphasizes managing product availability, especially in a context of rapid product... View Details
- Article
Humor as a Window into Generative AI Bias
By: R Samure, Julian De Freitas and Stefano Puntoni
A preregistered audit of 600 images by generative AI across 150 different prompts explores the link between humor and discrimination in consumer-facing AI solutions. When ChatGPT updates images to make them “funnier”, the prevalence of stereotyped groups changes. While... View Details
- 2023
- Chapter
Marketing Through the Machine’s Eyes: Image Analytics and Interpretability
By: Shunyuan Zhang, Flora Feng and Kannan Srinivasan
he growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility—if only the... View Details
Zhang, Shunyuan, Flora Feng, and Kannan Srinivasan. "Marketing Through the Machine’s Eyes: Image Analytics and Interpretability." Chap. 8 in Artificial Intelligence in Marketing. 20, edited by Naresh K. Malhotra, K. Sudhir, and Olivier Toubia. Review of Marketing Research. Emerald Publishing Limited, forthcoming.
- Research Summary
Overview
By: Iavor I. Bojinov
Over the last decade, technology companies like Amazon, Google, and Netflix have pioneered data-driven research and development processes centered on massive experimentation. However, as companies increase the breadth and scale of their experiments to millions of... View Details
- Research Summary
Overview
By: Jeremy Yang
Professor Yang's recent work focuses on understanding and solving important managerial problems in the creator economy. His projects are organized around the 4Cs: creator, content, community, and commerce. On creator, he studies creator inequality and bias. On content,... View Details
- Research Summary
Overview
By: Roberto Verganti
Roberto’s research focuses on how to create innovations that are meaningful for people, for society, and for their creators. He explores how leaders and organizations generate radically new visions, and make those visions come real. His studies lie at the intersection... View Details
- Forthcoming
- Article
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
By: Kosuke Imai and Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details
Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Journal of Business & Economic Statistics (forthcoming). (Pre-published online July 8, 2024.)