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All HBS Web
(116,010)
- Faculty Publications (3,245)
- 2024
- Conference Paper
Fair Machine Unlearning: Data Removal while Mitigating Disparities
By: Himabindu Lakkaraju, Flavio Calmon, Jiaqi Ma and Alex Oesterling
- January 2024
- Article
Population Interference in Panel Experiments
By: Kevin Wu Han, Guillaume Basse and Iavor Bojinov
The phenomenon of population interference, where a treatment assigned to one experimental unit affects another experimental unit’s outcome, has received considerable attention in standard randomized experiments. The complications produced by population interference in...
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Han, Kevin Wu, Guillaume Basse, and Iavor Bojinov. "Population Interference in Panel Experiments." Journal of Econometrics 238, no. 1 (January 2024).
- 2024
- Conference Paper
Quantifying Uncertainty in Natural Language Explanations of Large Language Models
By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal
Large Language Models (LLMs) are increasingly used as powerful tools for several
high-stakes natural language processing (NLP) applications. Recent prompting
works claim to elicit intermediate reasoning steps and key tokens that serve as
proxy explanations for LLM...
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Lakkaraju, Himabindu, Sree Harsha Tanneru, and Chirag Agarwal. "Quantifying Uncertainty in Natural Language Explanations of Large Language Models." Paper presented at the Society for Artificial Intelligence and Statistics, 2024.
- January–February 2024
- Article
Shared Service Delivery Can Increase Client Engagement: A Study of Shared Medical Appointments
By: Ryan W. Buell, Kamalini Ramdas, Nazlı Sönmez, Kavitha Srinivasan and Rengaraj Venkatesh
Problem Definition: Clients and service providers alike often consider one-on-one service delivery to be ideal, assuming – perhaps unquestioningly – that devoting individualized attention best improves client outcomes. In contrast, in shared service delivery, clients...
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Keywords:
Health Care and Treatment;
Customer Satisfaction;
Outcome or Result;
Performance Improvement
Buell, Ryan W., Kamalini Ramdas, Nazlı Sönmez, Kavitha Srinivasan, and Rengaraj Venkatesh. "Shared Service Delivery Can Increase Client Engagement: A Study of Shared Medical Appointments." Manufacturing & Service Operations Management 26, no. 1 (January–February 2024): 154–166.
- January 2024
- Article
Subset Scanning for Multi-Trait Analysis Using GWAS Summary Statistics
By: Rui Cao, Evan Olawsky, Edward McFowland III, Erin Marcotte, Logan Spector and Tianzhong Yang
Multi-trait analysis has been shown to have greater statistical power than single-trait analysis. Most of the existing multi-trait analysis methods only work with a limited number of traits and usually prioritize high statistical power over identifying relevant traits,...
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Cao, Rui, Evan Olawsky, Edward McFowland III, Erin Marcotte, Logan Spector, and Tianzhong Yang. "Subset Scanning for Multi-Trait Analysis Using GWAS Summary Statistics." Bioinformatics 40, no. 1 (January 2024).
- 2023
- Working Paper
Money, Time, and Grant Design
By: Kyle Myers and Wei Yang Tham
The design of research grants has been hypothesized to be a useful tool for
influencing researchers and their science. We test this by conducting two thought
experiments in a nationally representative survey of academic researchers. First,
we offer participants a...
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Myers, Kyle, and Wei Yang Tham. "Money, Time, and Grant Design." Harvard Business School Working Paper, No. 24-037, December 2023.
- 2023
- Working Paper
New Facts and Data about Professors and Their Research
By: Kyle Myers, Wei Yang Tham, Jerry Thursby, Marie Thursby, Nina Cohodes, Karim R. Lakhani, Rachel Mural and Yilun Xu
We introduce a new survey of professors at roughly 150 of the most research-intensive institutions of higher education in the US. We document seven new features of how research-active professors are compensated, how they spend their time, and how they perceive their...
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Keywords:
Research;
Higher Education;
Compensation and Benefits;
Measurement and Metrics;
Equality and Inequality;
Performance Productivity
Myers, Kyle, Wei Yang Tham, Jerry Thursby, Marie Thursby, Nina Cohodes, Karim R. Lakhani, Rachel Mural, and Yilun Xu. "New Facts and Data about Professors and Their Research." Harvard Business School Working Paper, No. 24-036, December 2023.
- December 15, 2023
- Article
What Every Leader Needs to Know About Carbon Credits
By: Varsha Ramesh Walsh and Michael W. Toffel
Many companies have begun to look into credits to offset their emissions as a way to support their net zero goals as their target years get closer and closer. As it stands, the carbon credit market is too small to bear the brunt of reducing companies’ impacts on the...
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Keywords:
Carbon Credits;
Climate;
Accounting;
Carbon Offsetting;
Carbon Abatement;
Carbon Emissions;
Carbon Footprint;
Climate Change;
Environmental Accounting;
Environmental Regulation
Ramesh Walsh, Varsha, and Michael W. Toffel. "What Every Leader Needs to Know About Carbon Credits." Harvard Business Review Digital Articles (December 15, 2023).
- December 2023
- Background Note
Organizational Learning
By: Willy Shih
This is a background note that surveys part of the extensive literature on organizational learning. The focus is on learning from experiences, how those learnings get translated into organizational routines and processes, and how that can also lead to getting stuck in...
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- December 4, 2023
- Comment
The Great Resignation, Employment, and Wages in Health Care
By: Amitabh Chandra and Louis-Jonas Heizlsperger
Notwithstanding concerns about staffing levels and burnout in health care, federal wage and employment data does not support the suggestion that a COVID-19 pandemic-related spike in quitting has had an enduring impact for hospitals or physician offices. Employment in...
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Chandra, Amitabh, and Louis-Jonas Heizlsperger. "The Great Resignation, Employment, and Wages in Health Care." NEJM Catalyst (December 4, 2023).
- December 18, 2023
- Article
Are Everywhere Stores the New Face of Retail?
By: David R. Bell, Santiago Gallino and Antonio Moreno
Historically, customer engagement and product fulfillment occurred in the same place — a traditional retail store. But today, retailers are beginning to explore how they can create opportunities for customers to engage with products in native environments. A related...
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Keywords:
Customer Focus and Relationships;
Consumer Behavior;
Distribution;
Logistics;
Retail Industry
Bell, David R., Santiago Gallino, and Antonio Moreno. "Are Everywhere Stores the New Face of Retail?" MIT Sloan Management Review (website) (December 18, 2023).
- 2023
- Article
Balancing Risk and Reward: An Automated Phased Release Strategy
By: Yufan Li, Jialiang Mao and Iavor Bojinov
Phased releases are a common strategy in the technology industry for gradually releasing new products or updates through a sequence of A/B tests in which the number of treated units gradually grows until full deployment or deprecation. Performing phased releases in a...
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Li, Yufan, Jialiang Mao, and Iavor Bojinov. "Balancing Risk and Reward: An Automated Phased Release Strategy." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 2023
- Article
Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset
By: Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu and Michael Lingzhi Li
Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam,...
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Keywords:
Large Language Model;
AI and Machine Learning;
Analytics and Data Science;
Health Industry
Liu, Junling, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, and Michael Lingzhi Li. "Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
- 2023
- Book
Beyond AI: ChatGPT, Web3, and the Business Landscape of Tomorrow
By: Ken Huang, Yang Wang, Feng Zhu, Xi Chen and Chunxiao Xing
This book explores the transformative potential of ChatGPT, Web3, and their impact on productivity and various industries. It delves into Generative AI (GenAI) and its representative platform ChatGPT, their synergy with Web3, and how they can revolutionize business...
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Huang, Ken, Yang Wang, Feng Zhu, Xi Chen, and Chunxiao Xing, eds. Beyond AI: ChatGPT, Web3, and the Business Landscape of Tomorrow. Springer, 2023.
- 2023
- Article
Dynamic HTA for Digital Health Solutions: Opportunities and Challenges for Patient-Centered Evaluation
By: Jan B. Brönneke, Annika Herr, Simon Reif and Ariel D. Stern
Germany’s 2019 Digital Healthcare Act (Digitale-Versorgung-Gesetz, or DVG) created a number of opportunities for the digital transformation of the health care delivery system. Key among these was the creation of a reimbursement pathway for patient-centered digital...
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Keywords:
Digital Transformation;
Applications and Software;
Product Development;
Insurance;
Policy;
Health Industry;
Germany
Brönneke, Jan B., Annika Herr, Simon Reif, and Ariel D. Stern. "Dynamic HTA for Digital Health Solutions: Opportunities and Challenges for Patient-Centered Evaluation." International Journal of Technology Assessment in Health Care 39, no. 1 (2023).
- December 2023
- Article
Introduction to the Special Section on Business and Climate Change
By: Rajesh Chandy, Glen Dowell, Colin Mayer, Erica Plambeck, George Serafeim, Michael W. Toffel, L. Beril Toktay and Elke Weber
Keywords:
Climate Change;
Adaptation;
Policy;
Corporate Social Responsibility and Impact;
Innovation and Invention;
Forecasting and Prediction
Chandy, Rajesh, Glen Dowell, Colin Mayer, Erica Plambeck, George Serafeim, Michael W. Toffel, L. Beril Toktay, and Elke Weber. "Introduction to the Special Section on Business and Climate Change." Management Science 69, no. 12 (December 2023): 7347–7351.
- 2023
- Article
M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities, and Models
By: Himabindu Lakkaraju, Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai and Haoyi Xiong
While Explainable Artificial Intelligence (XAI) techniques have been widely studied to explain predictions made by deep neural networks, the way to evaluate the faithfulness of explanation results remains challenging, due to the heterogeneity of explanations for...
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Keywords:
AI and Machine Learning
Lakkaraju, Himabindu, Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai, and Haoyi Xiong. "M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities, and Models." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 2023
- Article
MoPe: Model Perturbation-based Privacy Attacks on Language Models
By: Marvin Li, Jason Wang, Jeffrey Wang and Seth Neel
Recent work has shown that Large Language Models (LLMs) can unintentionally leak sensitive information present in their training data. In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training...
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Li, Marvin, Jason Wang, Jeffrey Wang, and Seth Neel. "MoPe: Model Perturbation-based Privacy Attacks on Language Models." Proceedings of the Conference on Empirical Methods in Natural Language Processing (2023): 13647–13660.
- 2023
- Article
Post Hoc Explanations of Language Models Can Improve Language Models
By: Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh and Himabindu Lakkaraju
Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks. Moreover, recent research has shown that incorporating human-annotated rationales (e.g., Chain-of-Thought prompting) during in-context learning can significantly enhance...
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Krishna, Satyapriya, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, and Himabindu Lakkaraju. "Post Hoc Explanations of Language Models Can Improve Language Models." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 2023
- Article
Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability
By: Usha Bhalla, Suraj Srinivas and Himabindu Lakkaraju
With the increased deployment of machine learning models in various real-world applications, researchers and practitioners alike have emphasized the need for explanations of model behaviour. To this end, two broad strategies have been outlined in prior literature to...
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Bhalla, Usha, Suraj Srinivas, and Himabindu Lakkaraju. "Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability." Advances in Neural Information Processing Systems (NeurIPS) (2023).