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All HBS Web
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- Faculty Publications (341)
- February 2018 (Revised October 2019)
- Case
HubSpot and Motion AI: Chatbot-Enabled CRM
By: Jill Avery and Thomas Steenburgh
HubSpot, an inbound marketing, sales, and customer relationship management (CRM) software provider, announced that it had acquired Motion AI, a software platform that enabled companies to easily build and deploy chatbots, fueled by artificial intelligence, to interact...
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Keywords:
CRM;
Sales Management;
Customer Service;
Artificial Intelligence;
B2B Vs. B2C;
Business Marketing;
SaaS;
Marketing;
Marketing Strategy;
Brands and Branding;
Customer Focus and Relationships;
Sales;
Salesforce Management;
Technological Innovation;
Applications and Software;
Customer Relationship Management;
AI and Machine Learning;
Technology Industry;
Service Industry;
United States;
North America
Avery, Jill, and Thomas Steenburgh. "HubSpot and Motion AI: Chatbot-Enabled CRM." Harvard Business School Case 518-067, February 2018. (Revised October 2019.)
- February 2018 (Revised June 2021)
- Case
New Constructs: Disrupting Fundamental Analysis with Robo-Analysts
By: Charles C.Y. Wang and Kyle Thomas
This case highlights the business challenges associated with a financial technology firm, New Constructs, that created a technology that can quickly parse complicated public firm financials to paint a clearer economic picture of firms, remove accounting distortions,...
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Keywords:
Fundamental Analysis;
Machine Learning;
Robo-analysts;
Financial Statements;
Financial Reporting;
Analysis;
Information Technology;
Accounting Industry;
Financial Services Industry;
Information Technology Industry;
North America;
Tennessee
Wang, Charles C.Y., and Kyle Thomas. "New Constructs: Disrupting Fundamental Analysis with Robo-Analysts." Harvard Business School Case 118-068, February 2018. (Revised June 2021.)
- February 2018
- Article
Retention Futility: Targeting High-Risk Customers Might Be Ineffective.
By: Eva Ascarza
Companies in a variety of sectors are increasingly managing customer churn proactively, generally by detecting customers at the highest risk of churning and targeting retention efforts towards them. While there is a vast literature on developing churn prediction models...
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Keywords:
Retention/churn;
Proactive Churn Management;
Field Experiments;
Heterogeneous Treatment Effect;
Machine Learning;
Customer Relationship Management;
Risk Management
Ascarza, Eva. "Retention Futility: Targeting High-Risk Customers Might Be Ineffective." Journal of Marketing Research (JMR) 55, no. 1 (February 2018): 80–98.
- January 2018 (Revised February 2023)
- Teaching Note
The Future of Patent Examination at the USPTO
This teaching note pairs with the case entitled: “The Future of Patent Examination at the USPTO” (case no. 617-027).
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- 2019
- Working Paper
Machine Learning Approaches to Facial and Text Analysis: Discovering CEO Oral Communication Styles
By: Prithwiraj Choudhury, Dan Wang, Natalie A. Carlson and Tarun Khanna
We demonstrate how a novel synthesis of three methods—(1) unsupervised topic modeling of text data to generate new measures of textual variance, (2) sentiment analysis of text data, and (3) supervised ML coding of facial images with a cutting-edge convolutional neural...
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Choudhury, Prithwiraj, Dan Wang, Natalie A. Carlson, and Tarun Khanna. "Machine Learning Approaches to Facial and Text Analysis: Discovering CEO Oral Communication Styles." Harvard Business School Working Paper, No. 18-064, January 2018. (Revised May 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...
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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.)
- Article
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
The most prevalent notions of fairness in machine learning are statistical definitions: they fix a small collection of pre-defined groups, and then ask for parity of some statistic of the classifier (like classification rate or false positive rate) across these groups....
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Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness." Proceedings of the International Conference on Machine Learning (ICML) 35th (2018).
- October 2017 (Revised April 2018)
- Case
Improving Worker Safety in the Era of Machine Learning (A)
By: Michael W. Toffel, Dan Levy, Jose Ramon Morales Arilla and Matthew S. Johnson
Managers make predictions all the time: How fast will my markets grow? How much inventory do I need? How intensively should I monitor my suppliers? Which potential customers will be most responsive to a particular marketing campaign? Which job candidates should I...
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Keywords:
Machine Learning;
Policy Implementation;
Empirical Research;
Inspection;
Occupational Safety;
Occupational Health;
Regulation;
Analysis;
Forecasting and Prediction;
Policy;
Operations;
Supply Chain Management;
Safety;
Manufacturing Industry;
Construction Industry;
United States
Toffel, Michael W., Dan Levy, Jose Ramon Morales Arilla, and Matthew S. Johnson. "Improving Worker Safety in the Era of Machine Learning (A)." Harvard Business School Case 618-019, October 2017. (Revised April 2018.)
- 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...
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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.)
- 14 Aug 2017
- Conference Presentation
A Convex Framework for Fair Regression
By: Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel and Aaron Roth
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems. These regularizers all enjoy convexity, permitting fast optimization, and they span the range from notions of group fairness to strong individual fairness. By varying...
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Berk, Richard, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. "A Convex Framework for Fair Regression." Paper presented at the 4th Workshop on Fairness, Accountability, and Transparency in Machine Learning, Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), August 14, 2017.
- June 2017
- Teaching Note
IBM Transforming, 2012–2016: Ginni Rometty Steers Watson
By: Rosabeth Moss Kanter and Jonathan Cohen
Ginni Rometty, who became IBM CEO in 2012, led efforts to transform the company around cognitive computing and the AI platform Watson. This Teaching Note helps instructors understand and teach the Harvard Business School case “IBM Transforming, 2012–2016: Ginni Rometty...
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- April 2017
- Case
The Future of Patent Examination at the USPTO
By: Prithwiraj Choudhury, Tarun Khanna and Sarah Mehta
The U.S. Patent and Trademark Office (USPTO) is the federal government agency responsible for evaluating and granting patents and trademarks. In 2015, the USPTO employed approximately 8,000 patent examiners who granted nearly 300,000 patents to inventors. As of April...
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Keywords:
Machine Learning;
Telework;
Collaborating With Unions;
Human Resources;
Recruitment;
Retention;
Intellectual Property;
Copyright;
Patents;
Trademarks;
Knowledge Sharing;
Technology Adoption;
Organizational Change and Adaptation;
Performance Productivity;
Performance Improvement;
District of Columbia
Choudhury, Prithwiraj, Tarun Khanna, and Sarah Mehta. "The Future of Patent Examination at the USPTO." Harvard Business School Case 617-027, April 2017.
- March 2017
- Supplement
Donna Dubinsky, Numenta and Artificial Intelligence
By: David B. Yoffie
Donna Dubinsky, CEO of Numenta, discusses her views of the future of artificial intelligence and the strategic challenges of building a new platform.
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Keywords:
Artificial Intelligence;
Strategy;
Technological Change;
AI and Machine Learning;
Technology Industry
Yoffie, David B. "Donna Dubinsky, Numenta and Artificial Intelligence." Harvard Business School Multimedia/Video Supplement 717-807, March 2017.
- Article
Why Boards Aren't Dealing with Cyberthreats
By: J. Yo-Jud Cheng and Boris Groysberg
Cheng, J. Yo-Jud, and Boris Groysberg. "Why Boards Aren't Dealing with Cyberthreats." Harvard Business Review (website) (February 22, 2017). (Excerpt featured in the Harvard Business Review. May–June 2017 "Idea Watch" section.)
- January 2017 (Revised March 2017)
- Case
IBM Transforming, 2012–2016: Ginni Rometty Steers Watson
By: Rosabeth Moss Kanter and Jonathan Cohen
To transform IBM for the next technology wave, Ginni Rometty, who became CEO in 2012, led divestment of declining businesses, made acquisitions in digital innovation and cloud computing, formed partnerships with former competitors such as Apple and tech startups, and...
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Keywords:
Digital;
Technological Change;
Artificial Intelligence;
Data;
IBM;
Watson;
Internet Of Things;
Innovation and Invention;
Management;
Sales;
Information Technology;
Technological Innovation;
Transformation;
AI and Machine Learning
Kanter, Rosabeth Moss, and Jonathan Cohen. "IBM Transforming, 2012–2016: Ginni Rometty Steers Watson." Harvard Business School Case 317-046, January 2017. (Revised March 2017.)
- Article
Learning Cost-Effective and Interpretable Treatment Regimes
By: Himabindu Lakkaraju and Cynthia Rudin
Lakkaraju, Himabindu, and Cynthia Rudin. "Learning Cost-Effective and Interpretable Treatment Regimes." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 20th (2017).
- 9 Dec 2016
- Conference Presentation
Learning Cost-Effective and Interpretable Regimes for Treatment Recommendation
By: Himabindu Lakkaraju and Cynthia Rudin
Lakkaraju, Himabindu, and Cynthia Rudin. "Learning Cost-Effective and Interpretable Regimes for Treatment Recommendation." Paper presented at the 30th Annual Conference on Neural Information Processing Systems (NIPS), Workshop on Interpretable Machine Learning in Complex Systems, Barcelona, Spain, December 9, 2016.
- 8 Dec 2016
- Conference Presentation
Learning Cost-Effective and Interpretable Treatment Regimes for Judicial Bail Decisions
By: Himabindu Lakkaraju and Cynthia Rudin
Lakkaraju, Himabindu, and Cynthia Rudin. "Learning Cost-Effective and Interpretable Treatment Regimes for Judicial Bail Decisions." Paper presented at the 30th Annual Conference on Neural Information Processing Systems (NIPS), Symposium on Machine Learning and the Law, Barcelona, Spain, December 8, 2016.
- 18 Nov 2016
- Conference Presentation
Rawlsian Fairness for Machine Learning
By: Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel and Aaron Leon Roth
Motivated by concerns that automated decision-making procedures can unintentionally lead to discriminatory behavior, we study a technical definition of fairness modeled after John Rawls' notion of "fair equality of opportunity". In the context of a simple model of...
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Joseph, Matthew, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Leon Roth. "Rawlsian Fairness for Machine Learning." Paper presented at the 3rd Workshop on Fairness, Accountability, and Transparency in Machine Learning, Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), November 18, 2016.
- July 2016
- Case
Spotify
By: Anita Elberse and Alexandre de Pfyffer
In November 2014, Spotify's chief content officer Ken Parks learns that record label Big Machine Records has requested the immediate removal of superstar artist Taylor Swift's entire catalogue from Spotify's music streaming service. Is it time for Spotify to reconsider...
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Keywords:
Entertainment;
Marketing;
Superstar;
Music;
Entertainment Marketing;
Media;
Digital Technology;
Creative Industries;
Product Portfolio Management;
General Management;
Management;
Strategy;
Internet and the Web;
Open Source Distribution;
Creativity;
Music Entertainment;
Product Marketing;
Music Industry
Elberse, Anita, and Alexandre de Pfyffer. "Spotify." Harvard Business School Case 516-046, July 2016.