Interpretable Machine Learning - Publications - Faculty & Research - Harvard Business School

Filter Results: (1,197)

Filter Results: (1,197)

Show Results For

← Page 7 of 1,197 Results →

October–December 2022
Article

Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius

Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed... View Details

Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; AI and Machine Learning; Forecasting and Prediction

Find at Harvard

Register to Read

November 2022
Article

A Language-Based Method for Assessing Symbolic Boundary Maintenance between Social Groups

By: Anjali M. Bhatt, Amir Goldberg and Sameer B. Srivastava

When the social boundaries between groups are breached, the tendency for people to erect and maintain symbolic boundaries intensifies. Drawing on extant perspectives on boundary maintenance, we distinguish between two strategies that people pursue in maintaining... View Details

Keywords: Culture; Machine Learning; Natural Language Processing; Symbolic Boundaries; Organizations; Boundaries; Social Psychology; Interpersonal Communication; Organizational Culture

Find at Harvard

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

2017
Working Paper

The Need for Speed: Effects of Uncertainty Reduction in Patenting

By: Mike Horia Teodorescu

Patents are essential in commerce to establish property rights for ideas and to give equal protection to firms that develop new technologies. Young firms especially depend on the protection of intellectual property to bring a product from concept to market. However,... View Details

Keywords: Startups; Natural Language Processing; Machine Learning; Patents; Business Startups; Risk and Uncertainty; Outcome or Result; Green Technology Industry

July 2019 (Revised November 2019)
Case

Osaro: Picking the Best Path

By: William R. Kerr, James Palano and Bastiane Huang

The founder of Osaro saw the potential of deep reinforcement learning to allow robots to be applied to new applications. Osaro targeted warehousing, already a dynamic industry for robotics and automation, for its initial product—a system which would allow robotic arms... View Details

October 2023 (Revised June 2024)
Case

ReUp Education: Can AI Help Learners Return to College?

By: Kris Ferreira, Christopher Thomas Ryan and Sarah Mehta

Founded in 2015, ReUp Education helps “stopped out students”—learners who have stopped making progress towards graduation—achieve their college completion goals. The company relies on a team of success coaches to engage with learners and help them reenroll. In 2019,... View Details

Keywords: AI; Algorithms; Machine Learning; Edtech; Education Technology; Analysis; Higher Education; AI and Machine Learning; Customization and Personalization; Failure; Education Industry; Technology Industry; United States

December 2020
Case

VIA Science (A)

By: Juan Alcácer, Rembrand Koning, Annelena Lobb and Kerry Herman

Via (a) captures the early days of the data analytics startup as founders Gounden and Ravanis considered which markets offer the right opportunities for their firm and what kinds of experiments will help them narrow their choice. Supplement Via (b) reveals the... View Details

Keywords: Data Analytics; Machine Learning; Artificial Intelligence; Strategy; Business Startups; Markets; AI and Machine Learning; Telecommunications Industry; Utilities Industry; United States; Japan

Article

Counterfactual Explanations Can Be Manipulated

By: Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju and Sameer Singh

Counterfactual explanations are useful for both generating recourse and auditing fairness between groups. We seek to understand whether adversaries can manipulate counterfactual explanations in an algorithmic recourse setting: if counterfactual explanations indicate... View Details

Keywords: Machine Learning Models; Counterfactual Explanations

Forthcoming
Article

Beefing IT Up for Your Investor? Engagement with Open Source Communities, Innovation, and Startup Funding: Evidence from GitHub

By: Annamaria Conti, Christian Peukert and Maria P. Roche

We study the engagement of nascent firms with open source communities and its implications for innovation and attracting funding. To do so, we link data on 160,065 U.S. startups from Crunchbase to their activities on the open source software development platform... View Details

Keywords: Startups; Knowledge; Open Source Communities; GitHub; Machine Learning; Innovation; Business Startups; Venture Capital; Information Technology; Strategy

Find at Harvard

Register to Read

September 2020 (Revised March 2022)
Case

JOANN: Joannalytics Inventory Allocation Tool

By: Kris Ferreira and Srikanth Jagabathula

Michael Joyce, Vice President of Inventory Management at JOANN, championed an effort to develop and implement an inventory allocation analytics tool that used advanced analytics to predict in-season demand of seasonal items for each of JOANN’s nearly 900 stores and... View Details

Keywords: Analytics; Machine Learning; Optimization; Inventory Management; Mathematical Methods; Decision Making; Operations; Supply Chain Management; Resource Allocation; Distribution; Technology Adoption; Applications and Software; Change Management; Fashion Industry; Consumer Products Industry; Retail Industry; United States; Ohio

October 2015 (Revised October 2016)
Case

Building Watson: Not So Elementary, My Dear! (Abridged)

By: Willy C. Shih

This case is set inside IBM Research's efforts to build a computer that can successfully take on human challengers playing the game show Jeopardy! It opens with the machine named Watson offering the incorrect answer "Toronto" to a seemingly simple question during the... View Details

Keywords: Analytics; Big Data; Business Analytics; Product Development Strategy; Machine Learning; Machine Intelligence; Artificial Intelligence; Product Development; AI and Machine Learning; Information Technology; Analytics and Data Science; Information Technology Industry; United States

October 20, 2020
Article

Expanding AI's Impact with Organizational Learning

By: Sam Ransbotham, Shervin Khodabandeh, David Kiron, François Candelon, Michael Chu and Burt LaFountain

Most companies developing AI capabilities have yet to gain significant financial benefits from their efforts. Only when organizations add the ability to learn with AI do significant benefits become likely. View Details

Keywords: AI and Machine Learning; Learning; Adoption

Register to Read

29 May 2013
Blog Post

Reflections and learnings

So, without further ado, the 7* lessons that made this all worthwhile for me: Winning is not easy: It’s easy to look at an industry leader like Sephora and imagine a seamlessly polished machine cranking out innovation after the next. Not... View Details

Keywords: Consumer Products / Retail

2024
Working Paper

Learning to Cover: Online Learning and Optimization with Irreversible Decisions

By: Alexander Jacquillat and Michael Lingzhi Li

Keywords: Buildings and Facilities; AI and Machine Learning; Geographic Location; Strategic Planning

January 2018 (Revised February 2023)
Teaching Note

The Future of Patent Examination at the USPTO

By: Prithwiraj Choudhury

This teaching note pairs with the case entitled: “The Future of Patent Examination at the USPTO” (case no. 617-027). View Details

Keywords: Machine Learning; Telework; Collaborating With Unions; Recruitment; Intellectual Property; Copyright; Patents; Trademarks; Knowledge Sharing; Technology Adoption; District of Columbia

2021
Working Paper

First Law of Motion: Influencer Video Advertising on TikTok

By: Jeremy Yang, Juanjuan Zhang and Yuhan Zhang

This paper engineers an intuitive feature that is predictive of the causal effect of influencer video advertising on product sales. We propose the concept of m-score, a summary statistic that captures the extent to which a product is advertised in the most engaging... View Details

Keywords: Influencer Advertising; Video Advertising; Computer Vision; Machine Learning; Advertising; Online Technology

2020
Book

Work, Mate, Marry, Love: How Machines Shape Our Human Destiny

By: Debora L. Spar

Covering a time frame that ranges from 8000 BC to the present, and drawing upon both Marxist and feminist theories, the book argues that nearly all the decisions we make in our most intimate lives—whom to marry, how to have children, how to have sex, how to think about... View Details

2024
Working Paper

Advancing Personalization: How to Experiment, Learn & Optimize

By: Aurelie Lemmens, Jason M.T. Roos, Sebastian Gabel, Eva Ascarza, Hernan Bruno, Elea McDonnell Feit, Brett Gordon, Ayelet Israeli, Carl F. Mela and Oded Netzer

Personalization has become the heartbeat of modern marketing. Advances in causal inference and machine learning enable companies to understand how the same marketing action can impact the choices of individual customers differently. This article provides an academic... View Details

Keywords: Personalization; Targeting; Experiments; Observational Studies; Policy Implementation; Policy Evaluation; Customization and Personalization; Marketing Strategy; AI and Machine Learning

2024
Working Paper

The Cram Method for Efficient Simultaneous Learning and Evaluation

By: Zeyang Jia, Kosuke Imai and Michael Lingzhi Li

We introduce the "cram" method, a general and efficient approach to simultaneous learning and evaluation using a generic machine learning (ML) algorithm. In a single pass of batched data, the proposed method repeatedly trains an ML algorithm and tests its empirical... View Details

Keywords: AI and Machine Learning

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... View Details

Keywords: Retention/churn; Proactive Churn Management; Field Experiments; Heterogeneous Treatment Effect; Machine Learning; Customer Relationship Management; Risk Management

Find at Harvard

←
7
8
…
59
60
→