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- February 2025
- Article
Disclosure, Humanizing, and Contextual Vulnerability of Generative AI Chatbots
By: Julian De Freitas and I. Glenn Cohen
In the wake of recent advancements in generative AI, regulatory bodies are trying to keep pace. One key decision is whether to require app makers to disclose the use of generative AI-powered chatbots in their products. We suggest that some generative AI-based chatbots... View Details
Keywords: AI and Machine Learning; Governing Rules, Regulations, and Reforms; Applications and Software; Well-being
De Freitas, Julian, and I. Glenn Cohen. "Disclosure, Humanizing, and Contextual Vulnerability of Generative AI Chatbots." New England Journal of Medicine AI 2, no. 2 (February 2025).
- January 2025
- Case
Duolingo: On a "Streak"
By: Jeffrey F. Rayport, Nicole Tempest Keller and Nicole Luo
In December 2024, Severin Hacker, Co-Founder and Chief Technology Officer of Duolingo, reflected on the remarkable evolution of the language-learning app he helped launch in 2011. As the #1 most downloaded education app in the world, Duolingo had over 100 million... View Details
- January 2025
- Technical Note
AI vs Human: Analyzing Acceptable Error Rates Using the Confusion Matrix
By: Tsedal Neeley and Tim Englehart
This technical note introduces the confusion matrix as a foundational tool in artificial intelligence (AI) and large language models (LLMs) for assessing the performance of classification models, focusing on their reliability for decision-making. A confusion matrix... View Details
- 2025
- Working Paper
Crossing the Design-Use Divide: How Process Manipulation Shapes the Design and Use of AI
By: Rebecca Karp
Existing literature often separates research on the design of innovations from their implementation and use, neglecting the role of selection—how organizations choose which innovations to implement. Although scholars suggest scientific approaches for selecting novel... View Details
Keywords: Decision Choices and Conditions; Technology Adoption; Groups and Teams; Prejudice and Bias
Karp, Rebecca. "Crossing the Design-Use Divide: How Process Manipulation Shapes the Design and Use of AI." Harvard Business School Working Paper, No. 25-034, January 2025.
- Working Paper
AI in Disguise—How AI-generated Ads' Visual Cues Shape Consumer Perception and Performance
By: Yannick Exner, Jochen Hartmann, Oded Netzer and Shunyuan Zhang
Generative AI’s recent advancements in creating content have offered vast potential to transform the advertising industry. This research investigates the impact of generative AI-enabled visual ad creation on real-world advertising effectiveness. For this purpose, we... View Details
Keywords: Digital Marketing; AI and Machine Learning; Advertising; Consumer Behavior; Advertising Industry
Exner, Yannick, Jochen Hartmann, Oded Netzer, and Shunyuan Zhang. "AI in Disguise—How AI-generated Ads' Visual Cues Shape Consumer Perception and Performance." SSRN Working Paper Series, No. 5096969.
- 2025
- Article
Humor as a Window into Generative AI Bias
By: Roger Samure, Julian De Freitas and Stefano Puntoni
A preregistered audit of 600 images by generative AI across 150 different prompts explores the link between humor and discrimination in consumer-facing AI solutions. When ChatGPT updates images to make them “funnier”, the prevalence of stereotyped groups changes. While... View Details
Samure, Roger, Julian De Freitas, and Stefano Puntoni. "Humor as a Window into Generative AI Bias." Art. 1326. Scientific Reports 15 (2025).
- January 2025
- Article
Reducing Prejudice with Counter-stereotypical AI
By: Erik Hermann, Julian De Freitas and Stefano Puntoni
Based on a review of relevant literature, we propose that the proliferation of AI with human-like and social features presents an unprecedented opportunity to address the underlying cognitive and affective drivers of prejudice. An approach informed by the psychology of... View Details
Keywords: Prejudice and Bias; AI and Machine Learning; Interpersonal Communication; Social and Collaborative Networks
Hermann, Erik, Julian De Freitas, and Stefano Puntoni. "Reducing Prejudice with Counter-stereotypical AI." Consumer Psychology Review 8, no. 1 (January 2025): 75–86.
- 2025
- Article
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
By: Kosuke Imai and Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details
Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Journal of Business & Economic Statistics 43, no. 1 (2025): 256–268.
- January–February 2025
- Article
Want Your Company to Get Better at Experimentation?: Learn Fast by Democratizing Testing
By: Iavor Bojinov, David Holtz, Ramesh Johari, Sven Schmit and Martin Tingley
For years, online experimentation has fueled the innovations of leading tech companies, enabling them to rapidly test and refine new ideas, optimize product features, personalize user experiences, and maintain a competitive edge. The widespread availability and lower... View Details
Keywords: Technological Innovation; AI and Machine Learning; Analytics and Data Science; Product Development; Competitive Advantage
Bojinov, Iavor, David Holtz, Ramesh Johari, Sven Schmit, and Martin Tingley. "Want Your Company to Get Better at Experimentation? Learn Fast by Democratizing Testing." Harvard Business Review 103, no. 1 (January–February 2025): 96–103.
- December 2024
- Case
Blue Cross Blue Shield of Michigan (BCBSM): The AI Journey
By: Shikhar Ghosh
In early 2024, Bill Fandrich, Executive VP and CIO of Blue Cross Blue Shield of Michigan (BCBSM), faced a critical decision about AI adoption within the organization. Fandrich had championed AI implementation at BCBSM. After successfully developing three AI... View Details
Keywords: AI; Machine Learning; Blue Cross; Automation; Digital Strategy; Digital Transformation; Generative Ai; Health Insurance; Insurance Companies; Innovation; IT Strategy; Leadership; Organizational Transformations; Technology; Non-profit; Michigan; AI and Machine Learning; Health; Health Industry; Michigan
- 2024
- Working Paper
Why Most Resist AI Companions
By: Julian De Freitas, Zeliha Oğuz-Uğuralp, Ahmet Kaan Uğuralp and Stefano Puntoni
Chatbots are now able to form emotional relationships with people and alleviate loneliness—a growing public health concern. Behavioral research provides little insight into whether everyday people are likely to use these applications and why. We address this question... View Details
Keywords: Generative Ai; Chatbots; Artificial Intelligence; Algorithmic Aversion; Lonelines; Technology Adoption; AI and Machine Learning; Well-being; Emotions
De Freitas, Julian, Zeliha Oğuz-Uğuralp, Ahmet Kaan Uğuralp, and Stefano Puntoni. "Why Most Resist AI Companions." Harvard Business School Working Paper, No. 25-030, December 2024. (Revised January 2025.)
- November 2024
- Case
AlphaGo (A): Birth of a New Intelligence
By: Shikhar Ghosh and Shweta Bagai
This case, the first of a three-part series, traces DeepMind's evolution from its 2010 founding through its acquisition by Google in 2014. Often referred to as the "Apollo project" of artificial intelligence, DeepMind used games as a testing ground to develop AI... View Details
- November 2024
- Supplement
AlphaGo (B): Birth of a New Intelligence
By: Shikhar Ghosh and Shweta Bagai
This case, the second in a three-part series, explores DeepMind's evolution from developing game-specific AI to more generalized learning systems. Following AlphaGo's 2017 victory over the Go world champion, DeepMind introduced two revolutionary systems that eliminated... View Details
- November 2024
- Supplement
AlphaGo (C): Birth of a New Intelligence
By: Shikhar Ghosh and Shweta Bagai
This case, the final of a three-part series, explores DeepMind's pivotal transition from mastering games to solving real-world scientific challenges. In December 2020, DeepMind's AI system AlphaFold 2 achieved a breakthrough by solving protein folding—a 50-year-old... View Details
Keywords: AI; Artificial Intelligence; Machine Learning; Research; Autonomy; Deep Learning; Drug Discovery; Healthcare Innovation; Neural Networks; Information Technology; Research And Development; Scientific Research; Technology Startup; AI and Machine Learning; Technological Innovation; United States
- November 2024
- Case
Cheerful Music
By: Shunyuan Zhang, Feng Zhu and Nancy Hua Dai
Established by Snow Jiang in 2019 in Shenzhen, China, Cheerful Music was a record label company that had created many hit songs in China. “Yi Xiao Jiang Hu,” its most famous hit song, gained 50 billion views on TikTok as the background music for the Subject Three... 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; Decision Making; Automation; Benefits; Compensation; Cost Reduction; Digital Transformation; Employment; Government; Fraud; Government Technology; Public Sector; Systems; Systems Integration; Unemployment Insurance; Waste Heat Recovery; AI and Machine Learning; Government Administration; Information Technology; Insurance; United States
- November–December 2024
- Article
Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing
By: Kirk Bansak and Elisabeth Paulson
This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a multi-year pilot in Switzerland, seeks to maximize the average predicted employment... View Details
Bansak, Kirk, and Elisabeth Paulson. "Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing." Operations Research 72, no. 6 (November–December 2024): 2375–2390.
- September–October 2024
- Article
The Crowdless Future? Generative AI and Creative Problem-Solving
The rapid advances in generative artificial intelligence (AI) open up attractive opportunities for creative problem-solving through human-guided AI partnerships. To explore this potential, we initiated a crowdsourcing challenge focused on sustainable, circular economy... View Details
Boussioux, Leonard, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic, and Karim R. Lakhani. "The Crowdless Future? Generative AI and Creative Problem-Solving." INFORMS Organization Science 35, no. 5 (September–October 2024): 1571–1955.
- September–October 2024
- Article
How AI Can Power Brand Management
By: Julian De Freitas and Elie Ofek
Marketers have begun experimenting with AI to improve their brand-management efforts. But unlike other marketing tasks, brand management involves more than just repeatedly executing one specialized function. Long considered the exclusive domain of creative talent, it... View Details
Keywords: Creativity; AI and Machine Learning; Brands and Branding; Product Positioning; Customer Focus and Relationships
De Freitas, Julian, and Elie Ofek. "How AI Can Power Brand Management." Harvard Business Review 102, no. 5 (September–October 2024): 108–114.
- 2024
- Article
Learning Under Random Distributional Shifts
By: Kirk Bansak, Elisabeth Paulson and Dominik Rothenhäusler
Algorithmic assignment of refugees and asylum seekers to locations within host
countries has gained attention in recent years, with implementations in the U.S.
and Switzerland. These approaches use data on past arrivals to generate machine
learning models that can... View Details
Bansak, Kirk, Elisabeth Paulson, and Dominik Rothenhäusler. "Learning Under Random Distributional Shifts." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 27th (2024).