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- September–October 2021
- Article
Frontiers: Can an AI Algorithm Mitigate Racial Economic Inequality? An Analysis in the Context of Airbnb
By: Shunyuan Zhang, Nitin Mehta, Param Singh and Kannan Srinivasan
We study the effect of Airbnb’s smart-pricing algorithm on the racial disparity in the daily revenue earned by Airbnb hosts. Our empirical strategy exploits Airbnb’s introduction of the algorithm and its voluntary adoption by hosts as a quasi-natural experiment. Among...
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- 2021
- Working Paper
Invisible Primes: Fintech Lending with Alternative Data
By: Marco Di Maggio, Dimuthu Ratnadiwakara and Don Carmichael
We exploit anonymized administrative data provided by a major fintech platform to investigate whether using alternative data to assess borrowers’ creditworthiness results in broader credit access. Comparing actual outcomes of the fintech platform’s model to...
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- September 24, 2021
- Article
A Labor Movement for the Platform Economy
By: Li Jin, Scott Duke Kominers and Lila Shroff
Platforms are fundamentally changing the contract between workers and companies—and the workers and creatives that create value for platform companies, and rely on platforms for their livelihoods, often have little power when it comes to getting their concerns...
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- September 17, 2021
- Article
AI Can Help Address Inequity—If Companies Earn Users' Trust
By: Shunyuan Zhang, Kannan Srinivasan, Param Singh and Nitin Mehta
While companies may spend a lot of time testing models before launch, many spend too little time considering how they will work in the wild. In particular, they fail to fully consider how rates of adoption can warp developers’ intent. For instance, Airbnb launched a...
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- 2021
- Working Paper
The Demand for Executive Skills
By: Stephen Hansen, Raffaella Sadun, Tejas Ramdas and Joseph B. Fuller
We use a unique corpus of job descriptions for C-suite positions to document skills requirements in top managerial occupations across a large sample of firms. A novel algorithm maps the text of each executive search into six separate skill clusters reflecting...
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- Article
The Pitfalls of Pricing Algorithms: Be Mindful of How They Can Hurt Your Brand
By: Marco Bertini and Oded Koenigsberg
More and more companies are relying on pricing algorithms to maximize profits. The use of artificial intelligence and machine learning enables real-time price adjustments based on supply and demand, competitors’ activities, delivery schedules, and so forth. But...
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- 2021
- Working Paper
BEAT Unintended Bias in Personalized Policies
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" attributes....
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- August 2021 (Revised September 2021)
- Case
Intenseye: Powering Workplace Health and Safety with AI
By: Michael W. Toffel and Youssef Abdel Aal
Intenseye was a Turkey-based technology startup that deployed machine learning algorithms to workplace camera feeds in order to identify unsafe worker actions and unsafe working conditions, in order to help improve worker safety. The case describes how Intenseye’s...
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- 2021
- Working Paper
The Demand for Executive Skills
By: Stephen Hansen, Tejas Ramdas, Raffaella Sadun and Joseph B. Fuller
We use a unique corpus of job descriptions for C-suite positions to document skills requirements in top managerial occupations across a large sample of firms. A novel algorithm maps the text of each executive search into six separate skill clusters reflecting...
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- 2021
- Article
To Thine Own Self Be True? Incentive Problems in Personalized Law
By: Jordan M. Barry, John William Hatfield and Scott Duke Kominers
Recent years have seen an explosion of scholarship on “personalized law.” Commentators foresee a world in which regulators armed with big data and machine learning techniques determine the optimal legal rule for every regulated party, then instantaneously disseminate...
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- Article
Use of Connected Digital Products in Clinical Research Following the COVID-19 Pandemic: A Comprehensive Analysis of Clinical Trials
By: Caroline Marra, William J. Gordon and Ariel Dora Stern
Objectives: In an effort to mitigate COVID-19 related challenges for clinical research, the U.S. Food and Drug Administration (FDA) issued new guidance for the conduct of ‘virtual’ clinical trials in late March 2020. This study documents trends in the use of...
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- May 2021 (Revised October 2021)
- Teaching Note
THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)
By: Ayelet Israeli and Jill Avery
THE YES, a multi-brand shopping app launched in May 2020 offered a new type of buying experience for women’s fashion, driven by a sophisticated algorithm that used data science and machine learning to create and deliver a personalized store for every shopper, based on...
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Keywords:
Data;
Data Analytics;
Artificial Intelligence;
AI;
AI Algorithms;
AI Creativity;
Fashion;
Retail;
Retail Analytics;
Digital Marketing;
E-commerce;
E-Commerce Strategy;
Platform;
Platforms;
Big Data;
Preference Elicitation;
Predictive Analytics;
App Development;
＂Marketing Analytics＂;
Advertising;
Mobile App;
Mobile Marketing;
Apparel;
Referral Rewards;
Referrals;
Female Ceo;
Female Entrepreneur;
Female Protagonist;
Data and Data Sets;
Analysis;
Creativity;
Marketing Strategy;
Brands and Branding;
Consumer Behavior;
Demand and Consumers;
Forecasting and Prediction;
Marketing Channels;
Online Advertising;
Online Technology;
Mobile Technology;
Fashion Industry;
Retail Industry;
Apparel and Accessories Industry;
Consumer Products Industry;
United States
- 2021
- Article
Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring
By: Tom Sühr, Sophie Hilgard and Himabindu Lakkaraju
Ranking algorithms are being widely employed in various online hiring platforms including LinkedIn, TaskRabbit, and Fiverr. Prior research has demonstrated that ranking algorithms employed by these platforms are prone to a variety of undesirable biases, leading to the...
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- 2021
- Article
Fair Influence Maximization: A Welfare Optimization Approach
By: Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Max Izenberg, Ryan Brown, Eric Rice and Milind Tambe
Several behavioral, social, and public health interventions, such as suicide/HIV prevention or community preparedness against natural disasters, leverage social network information to maximize outreach. Algorithmic influence maximization techniques have been proposed...
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- March 2021
- Supplement
Artea (A), (B), (C), and (D): Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
Power Point Supplement to Teaching Note for HBS No. 521-021,521-022,521-037,521-043. This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on...
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- 2021
- Working Paper
Time Dependency, Data Flow, and Competitive Advantage
Data is fundamental to machine learning-based products and services and is considered strategic due to its externalities for businesses, governments, non-profits, and more generally for society. It is renowned that the value of organizations (businesses, government...
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- March 2021
- Case
VideaHealth: Building the AI Factory
By: Karim R. Lakhani and Amy Klopfenstein
Florian Hillen, co-founder and CEO of VideaHealth, a startup that used artificial intelligence (AI) to detect dental conditions on x-rays, spent the early years of his company laying the groundwork for an AI factory. A process for quickly building and iterating on new...
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Keywords:
Artificial Intelligence;
Innovation and Invention;
Disruptive Innovation;
Technological Innovation;
Technology;
Software;
Technology Adoption;
Technology Platform;
Entrepreneurship;
Technology Industry;
Medical Devices and Supplies Industry;
North and Central America;
United States;
Massachusetts;
Cambridge
- Mar 2021
- Conference Presentation
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
By: Seth Neel, Aaron Leon Roth and Saeed Sharifi-Malvajerdi
We study the data deletion problem for convex models. By leveraging techniques from convex optimization and reservoir sampling, we give the first data deletion algorithms that are able to handle an arbitrarily long sequence of adversarial updates while promising both...
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- 2021
- Working Paper
Can I Still Trust You? Understanding the Impact of Distribution Shifts on Algorithmic Recourses
By: Kaivalya Rawal, Ece Kamar and Himabindu Lakkaraju
- 2021
- Working Paper
Manipulated Counterfactuals: Risks of Hidden Biases in Algorithmic Recourse
By: Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju and Sameer Singh