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Show Results For
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All HBS Web
(1,035)
- People (1)
- News (209)
- Research (557)
- Events (8)
- Multimedia (7)
- Faculty Publications (446)
Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development
Predictive model development is understudied despite its importance to modern businesses. Although prior discussions highlight advances in methods (along the dimensions of data, computing power, and algorithms) as the primary driver of model quality, the value of...
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- January 2021
- Case
Anodot: Autonomous Business Monitoring
By: Antonio Moreno and Danielle Golan
Autonomous business monitoring platform Anodot leveraged machine learning to provide real-time alerts regarding business anomalies. Anodot’s solution was used in various industries in order to primarily monitor business health, such as revenue and payments, product...
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Keywords:
Digital Platforms;
Internet and the Web;
Knowledge Sharing;
Information Management;
Sales;
Value Creation;
Product Positioning;
Israel
Moreno, Antonio, and Danielle Golan. "Anodot: Autonomous Business Monitoring." Harvard Business School Case 621-084, January 2021.
- May 2022 (Revised June 2024)
- Case
LOOP: Driving Change in Auto Insurance Pricing
By: Elie Ofek and Alicia Dadlani
John Henry and Carey Anne Nadeau, co-founders and co-CEOs of LOOP, an insurtech startup based in Austin, Texas, were on a mission to modernize the archaic $250 billion automobile insurance market. They sought to create equitably priced insurance by eliminating pricing...
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Keywords:
AI and Machine Learning;
Technological Innovation;
Equality and Inequality;
Prejudice and Bias;
Growth and Development Strategy;
Customer Relationship Management;
Price;
Insurance Industry;
Financial Services Industry
Ofek, Elie, and Alicia Dadlani. "LOOP: Driving Change in Auto Insurance Pricing." Harvard Business School Case 522-073, May 2022. (Revised June 2024.)
Work‐from‐anywhere: The productivity effects of geographic flexibility
An emerging form of remote work allows employees to work‐from‐anywhere, so that the worker can choose to live in a preferred geographic location. While traditional work‐from‐home (WFH) programs offer the worker temporal flexibility,...
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- 02 Jul 2019
- Blog Post
Summer Listening - HBS Podcasts to Take to the Beach
nature of work. Prediction: How AI will affect Business, Work, and LifeHow should we think about improvements in artificial intelligence? Bill speaks with Joshua Gans, co-author of "Prediction Machines: The...
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- Program
Competing in the Age of AI—Virtual
will delve into diverse applications of AI, machine learning, predictive modeling, and data science; explore network effects and platform strategies; View Details
- Research Summary
Overview
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...
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- 22 Oct 2007
- Research & Ideas
Bringing ‘Lean’ Principles to Service Industries
"waterfall" method in which each step of the process is completed in turn by a separate worker. By sharing mistakes across the process, the customer and project team members benefit individually View Details
- 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...
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- 21 Nov 2022
- Blog Post
HBS Student Club Spotlight: Sustainability Club
In this blog, hear from 2022-23 Sustainability Club co-presidents: Angela Son, Erin O’ Malley & Meghavi Talati (all MBA 2023). What are the mission and activities of your club? The Sustainability Club is a place where all students,...
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- 15 Oct 2001
- Op-Ed
Lessons from the Rubble
Pundits and investors spoke giddily of the end of national borders, of markets that spanned the globe and replaced the hefty weight of machines View Details
Keywords:
by Debora L. Spar
- 2023
- Working Paper
In-Context Unlearning: Language Models as Few Shot Unlearners
By: Martin Pawelczyk, Seth Neel and Himabindu Lakkaraju
Machine unlearning, the study of efficiently removing the impact of specific training points on the
trained model, has garnered increased attention of late, driven by the need to comply with privacy
regulations like the Right to be Forgotten. Although unlearning is...
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Pawelczyk, Martin, Seth Neel, and Himabindu Lakkaraju. "In-Context Unlearning: Language Models as Few Shot Unlearners." Working Paper, October 2023.
Eliminating unintended bias in personalized policies using Bias Eliminating Adapted Trees (BEAT) - PNAS
An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those... View Details
- Web
Publications - Faculty & Research
Entrepreneurship; Fairness; Cross-Cultural and Cross-Border Issues; Global Strategy; Growth and Development; Information Technology; AI and View Details
- Web
HBS Working Knowledge – Harvard Business School Faculty Research
AI in Adapting to Change by Rachel Layne 26 MAR 2024 | Research & Ideas Could artificial intelligence systems eventually perform surgeries or fly planes? First, AI will have to View Details
- Article
Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)
By: Eva Ascarza and Ayelet Israeli
An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”... View Details
Keywords:
Algorithm Bias;
Personalization;
Targeting;
Generalized Random Forests (GRF);
Discrimination;
Customization and Personalization;
Decision Making;
Fairness;
Mathematical Methods
Ascarza, Eva, and Ayelet Israeli. "Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)." e2115126119. Proceedings of the National Academy of Sciences 119, no. 11 (March 8, 2022).
- 08 Mar 2011
- First Look
First Look: March 8
relocate to Japan and compete with other world-class international business schools. Purchase this case:http://cb.hbsp.harvard.edu/cb/product/811061-PDF-ENG The Wright Brothers and Their Flying View Details
Keywords:
Sean Silverthorne
- 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...
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Graphic Packaging: Project Cowboy
In July 2019, Graphic Packaging CEO Michael Doss was proposing a $600 million investment in a new machine to produce coated recycled board (CRB), a type of paper packaging used for consumer products (cups, cereal boxes, beverage boxes, etc.) that utilized recycled...
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- 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.)