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- May 2025
- Teaching Note
The VideaHealth AI Factory: CEO Florian Hillen on Speed, Scale, and Innovation
By: Tsedal Neeley
Teaching Note for HBS Case No. 425-720. Florian Hillen, co-founder and CEO of VideaHealth, a startup using artificial intelligence (AI) to detect dental conditions on x-rays, spent the early years of his company laying the groundwork for an AI factory. This AI factory,... View Details
Keywords: Diagnostics; Organization Design; Change Management; Disruption; Transformation; Health Care and Treatment; AI and Machine Learning; Technological Innovation; Technology Adoption; Disruptive Innovation; Management Style; Organizational Culture; Success; Adoption; Technology Industry; Health Industry; United States
- March 2025
- Case
Niramai: An AI Solution to Save Lives
By: Rembrand Koning, Maria P. Roche and Kairavi Dey
Founded in 2017, Niramai developed Thermalytix, a breast cancer screening tool. Thermalytix used a high-resolution thermal sensing device and machine learning algorithms to analyze thermal images and detect tumors. Its patented solution leveraged big data analytics,... View Details
- March 2025
- Supplement
Thrive Earlier Detection (B) and the Evolution of Catalio Capital Management
By: Malcolm Baker, Tara Bozzini and William Vrattos
- January 2025
- Case
The VideaHealth AI Factory: CEO Florian Hillen on Speed, Scale, and Innovation (A)
By: Tsedal Neeley, Levi Stroud, Ruth Page and Dave Habeeb
Pre-abstract:
This multimedia case should be assigned to students in advance of class. Instructors should consider the timing of making the (B) Case videos available to students, as they may reveal key case details.
Abstract: Florian Hillen, co-founder... View Details
Abstract: Florian Hillen, co-founder... View Details
- January 2025
- Supplement
The VideaHealth AI Factory: CEO Florian Hillen on Speed, Scale, and Innovation (B)
By: Tsedal Neeley, Levi Stroud, Ruth Page and Dave Habeeb
Pre-abstract:
This multimedia case should be assigned to students in advance of class. Instructors should consider the timing of making the (B) Case videos available to students, as they may reveal key case details.
Abstract: Florian Hillen, co-founder... View Details
Abstract: Florian Hillen, co-founder... View Details
- November 2024 (Revised January 2025)
- Case
MiDAS: Automating Unemployment Benefits
By: Shikhar Ghosh and Shweta Bagai
In 2015, the state of Michigan considered whether to nominate its Michigan Integrated Data Automated System (MiDAS) for a prestigious state technology award. Launched in 2013 amid severe budget pressures, the $47 million automated fraud detection system was designed to... View Details
Keywords: Artificial Intelligence; AI; Machine Learning Models; Algorithmic Data; Automation; Benefits; Compensation; Cost Reduction; Government; Fraud; Government Technology; Public Sector; Systems; Systems Integration; Unemployment Insurance; Waste Heat Recovery; AI and Machine Learning; Government Administration; Insurance; Decision Making; Digital Transformation; Employment; Public Administration Industry; United States; Michigan
- 2024
- Working Paper
AI Companions Reduce Loneliness
By: Julian De Freitas, Ahmet K. Uğuralp, Zeliha O. Uğuralp and Stefano Puntoni
Chatbots are now able to engage in sophisticated conversations with consumers in the domain of relationships, providing a potential coping solution to widescale societal loneliness. Behavioral research provides little insight into whether these applications are... View Details
- April 2024
- Article
Detecting Routines: Applications to Ridesharing CRM
By: Ryan Dew, Eva Ascarza, Oded Netzer and Nachum Sicherman
Routines shape many aspects of day-to-day consumption. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines—which we define as repeated behaviors with recurring, temporal... View Details
- 2024
- Working Paper
Greenlighting Innovative Projects: How Evaluation Format Shapes the Perceived Feasibility of Novel Ideas
By: Jacqueline N. Lane, Tianxi Cai, Michael Menietti, Griffin Weber and Eva C. Guinan
Evaluation of novel projects is essential for scientific and technological advancement. However,
evaluator bias toward a project’s potential can obscure its limitations. This study investigates
evaluation formats by contrasting combined assessments of novelty and... View Details
- March 2024
- Case
Unintended Consequences of Algorithmic Personalization
By: Eva Ascarza and Ayelet Israeli
“Unintended Consequences of Algorithmic Personalization” (HBS No. 524-052) investigates algorithmic bias in marketing through four case studies featuring Apple, Uber, Facebook, and Amazon. Each study presents scenarios where these companies faced public criticism for... View Details
- February 2024
- Course Overview Note
The Anatomy of Fraud
By: Jonas Heese
Corporate fraud remains a serious problem. Learning how to detect and prevent it, and make better investment decisions, has broad applicability for private and public market investors, as well as for people joining or running companies. This course note describes a... View Details
- February 2024
- Article
Conveying and Detecting Listening in Live Conversation
By: Hanne Collins, Julia A. Minson, Ariella S. Kristal and Alison Wood Brooks
Across all domains of human social life, positive perceptions of conversational listening (i.e., feeling heard) predict well-being, professional success, and interpersonal flourishing. But a fundamental question remains: Are perceptions of listening accurate? Prior... View Details
- January 2024
- Case
Post-Wirecard: BaFin under Mark Branson
By: Jonas Heese, Carlota Moniz and Daniela Beyersdorfer
In November 2023, Mark Branson, the head of Germany's Federal Financial Supervisory Authority (BaFin), reflected on the efficacy of the reforms initiated since the Wirecard scandal. BaFin had been discredited after Wirecard’s downfall in 2020. The press had derided it... View Details
- December 2023
- Case
The Valuation Multiple Detective
By: Jonas Heese, Paul M. Healy and Pietro Bonetti
- 2023
- Working Paper
Black-box Training Data Identification in GANs via Detector Networks
By: Lukman Olagoke, Salil Vadhan and Seth Neel
Since their inception Generative Adversarial Networks (GANs) have been popular generative models across images, audio, video, and tabular data. In this paper we study whether given access to a trained GAN, as well as fresh samples from the underlying distribution, if... View Details
- 2023
- Working Paper
Much Ado About Nothing? Overreaction to Random Regulatory Audits
By: Samuel Antill and Joseph Kalmenovitz
Regulators often audit firms to detect non-compliance. Exploiting a natural experiment in the lobbying industry, we show that firms overreact to audits and this response distorts prices and reduces welfare. Each year, federal regulators audit a random sample of... View Details
- 2023
- Working Paper
Learning to Use: Stack Overflow and Technology Adoption
By: Daniel Jay Brown and Maria P. Roche
In this paper, we examine the potential impact of Q&A websites on the adoption of technologies.
Using data from Stack Overflow – one of the most popular Q&A websites worldwide
– and implementing an instrumental-variable approach, we find that users whose questions... View Details
- 2025
- Working Paper
Managing Remote Work Quality: Evidence from Auditing Management Systems Standards
By: Ashley Palmarozzo, Michael W. Toffel and Melissa Ouellet
Remote work has become more common, providing operational flexibility and productivity benefits, but questions remain about whether and how it affects work quality. We investigate the quality effects of remote work in a context in which remote work separates workers... View Details
- 2023
- Working Paper
Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness
By: Neil Menghani, Edward McFowland III and Daniel B. Neill
In this paper, we develop a new criterion, "insufficiently justified disparate impact" (IJDI), for assessing whether recommendations (binarized predictions) made by an algorithmic decision support tool are fair. Our novel, utility-based IJDI criterion evaluates false... View Details
- 2023
- Working Paper
Auditing Predictive Models for Intersectional Biases
By: Kate S. Boxer, Edward McFowland III and Daniel B. Neill
Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for individuals at the intersection of two or more protected classes. To address this risk, we... View Details