Publications
Publications
Show Results For
- All HBS Web
(10,864)
- Faculty Publications (925)
- August 2023
- Article
Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel
By: Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju and Sameer Singh
Practitioners increasingly use machine learning (ML) models, yet models have become more complex and harder to understand. To understand complex models, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use... View Details
- July–August 2023
- Article
Investing in Growth Through Uncertainty
By: Ranjay Gulati
When faced with disruptions and downturns, many leaders and companies instinctively focus on cutting costs to maintain profitability. But some identify opportunities and then take thoughtful action to emerge from crisis even stronger. That means not only planning for... View Details
- 2023
- Article
Towards Bridging the Gaps between the Right to Explanation and the Right to Be Forgotten
By: Himabindu Lakkaraju, Satyapriya Krishna and Jiaqi Ma
The Right to Explanation and the Right to be Forgotten are two important principles outlined to regulate algorithmic decision making and data usage in real-world applications. While the right to explanation allows individuals to request an actionable explanation for an... 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
- July 2023
- Case
HealthVerity: Real World Data and Evidence
By: Satish Tadikonda
Andrew Kress (CEO and founder) and his team had built a promising marketplace business at HealthVerity serving its core market in healthcare, with a focus on pharmaceutical R&D and services. Thus far, HealthVerity’s products had been unique to the pharma and pharma... View Details
- July 2023 (Revised July 2023)
- Background Note
Generative AI Value Chain
By: Andy Wu and Matt Higgins
Generative AI refers to a type of artificial intelligence (AI) that can create new content (e.g., text, image, or audio) in response to a prompt from a user. ChatGPT, Bard, and Claude are examples of text generating AIs, and DALL-E, Midjourney, and Stable Diffusion are... View Details
- July 2023
- Article
Managerial Quality and Productivity Dynamics
By: Achyuta Adhvaryu, Anant Nyshadham and Jorge Tamayo
Do productivity and managerial quality vary within the firm? If so which managerial traits and practices matter most for team productivity? Combining granular garment production data with survey data on managers across 120 production lines in India, we document... View Details
- 2023
- Working Paper
The Complexity of Economic Decisions
By: Xavier Gabaix and Thomas Graeber
We propose a theory of the complexity of economic decisions. Leveraging a macroeconomic framework of production functions, we conceptualize the mind as a cognitive economy, where a task’s complexity is determined by its composition of cognitive operations. Complexity... View Details
- June 2023 (Revised November 2024)
- Case
Albert Einstein: Changing the World
By: Robert Simons and Shirley Sun
This case traces the rise of Albert Einstein from a small town in Germany to a towering intellectual leader who revolutionized the field of physics. The case describes his early education and his penchant for individual thinking and non-conformity. A committed... 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
Evaluation and Learning in R&D Investment
By: Alexander P. Frankel, Joshua L. Krieger, Danielle Li and Dimitris Papanikolaou
We examine the role of spillover learning in shaping the value of exploratory versus incremental
R&D. Using data from drug development, we show that novel drug candidates generate more
knowledge spillovers than incremental ones. Despite being less likely to reach... View Details
- 2023
- Article
Provable Detection of Propagating Sampling Bias in Prediction Models
By: Pavan Ravishankar, Qingyu Mo, Edward McFowland III and Daniel B. Neill
With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider... View Details
- May–June 2023
- Article
A New Approach to Building Your Personal Brand: How to Communicate Your Value
By: Jill Avery and Rachel Greenwald
For better or worse, in today’s world everyone is a brand. Whether you’re applying for a job, asking for a promotion, or writing a dating profile, your success will depend on getting others to recognize your value. So you need to get comfortable marketing... View Details
- 2023
- Article
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju
As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely impacted (e.g., loan denied) by the predictions of these models are provided with a means... View Details
- 2024
- Working Paper
Using LLMs for Market Research
By: James Brand, Ayelet Israeli and Donald Ngwe
Large language models (LLMs) have rapidly gained popularity as labor-augmenting
tools for programming, writing, and many other processes that benefit from quick text
generation. In this paper we explore the uses and benefits of LLMs for researchers and
practitioners... View Details
- April 2023 (Revised February 2024)
- Case
AI Wars
By: Andy Wu, Matt Higgins, Miaomiao Zhang and Hang Jiang
In February 2024, the world was looking to Google to see what the search giant and long-time putative technical leader in artificial intelligence (AI) would do to compete in the massively hyped technology of generative AI. Over a year ago, OpenAI released ChatGPT, a... View Details
- 2023
- Working Paper
Feature Importance Disparities for Data Bias Investigations
By: Peter W. Chang, Leor Fishman and Seth Neel
It is widely held that one cause of downstream bias in classifiers is bias present in the training data. Rectifying such biases may involve context-dependent interventions such as training separate models on subgroups, removing features with bias in the collection... View Details
- April 2023
- Article
Inattentive Inference
By: Thomas Graeber
This paper studies how people infer a state of the world from information structures that include additional, payoff-irrelevant states. For example, learning from a customer review about a product’s quality requires accounting for the reviewer’s otherwise irrelevant... View Details
- April 2023
- Article
On the Privacy Risks of Algorithmic Recourse
By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected... View Details
- March–April 2023
- Article
Pricing for Heterogeneous Products: Analytics for Ticket Reselling
By: Michael Alley, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li and Georgia Perakis
Problem definition: We present a data-driven study of the secondary ticket market. In particular, we are primarily concerned with accurately estimating price sensitivity for listed tickets. In this setting, there are many issues including endogeneity, heterogeneity in... View Details