About

Robert P Bartlett is the William A. Franke Professor of Law and Business at Stanford University. He is also a Senior Fellow at the Stanford Institute for Economic Policy Research and holds a courtesy appointment as a Professor of Finance at the Stanford Graduate School of Business. His roles indicate a focus on the intersection of law, business, and economic policy, contributing to academic leadership and research in these areas.

Research topics

• Computer Science
• Political Science
• Artificial Intelligence
• Medical education
• Applied psychology
• Psychology
• World Wide Web
• Medicine

Selected publications

• A Profit-Based Measure of Lending Discrimination

  ArXiv.org · 2025-12-23

  articleOpen access

  Algorithmic lending has transformed the consumer credit landscape, with complex machine learning models now commonly used to make or assist underwriting decisions. To comply with fair lending laws, these algorithms typically exclude legally protected characteristics, such as race and gender. Yet algorithmic underwriting can still inadvertently favor certain groups, prompting new questions about how to audit lending algorithms for potentially discriminatory behavior. Building on prior theoretical work, we introduce a profit-based measure of lending discrimination in loan pricing. Applying our approach to approximately 80,000 personal loans from a major U.S. fintech platform, we find that loans made to men and Black borrowers yielded lower profits than loans to other groups, indicating that men and Black applicants benefited from relatively favorable lending decisions. We trace these disparities to miscalibration in the platform's underwriting model, which underestimates credit risk for Black borrowers and overestimates risk for women. We show that one could correct this miscalibration -- and the corresponding lending disparities -- by explicitly including race and gender in underwriting models, illustrating a tension between competing notions of fairness.

  PublisherOA PDF

• When Disclosure Pays: Evidence from the Over-The-Counter Markets 

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• A Fractional Solution to a Stock Market Mystery

  Financial Analysts Journal · 2025-05-21 · 1 citations

  article1st author
  PublisherDOI

• Preferred Stock Liquidation Preferences

  2025-01-01 · 1 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• A Profit-Based Measure of Lending Discrimination

  arXiv (Cornell University) · 2025-12-23

  preprintOpen access

  Algorithmic lending has transformed the consumer credit landscape, with complex machine learning models now commonly used to make or assist underwriting decisions. To comply with fair lending laws, these algorithms typically exclude legally protected characteristics, such as race and gender. Yet algorithmic underwriting can still inadvertently favor certain groups, prompting new questions about how to audit lending algorithms for potentially discriminatory behavior. Building on prior theoretical work, we introduce a profit-based measure of lending discrimination in loan pricing. Applying our approach to approximately 80,000 personal loans from a major U.S. fintech platform, we find that loans made to men and Black borrowers yielded lower profits than loans to other groups, indicating that men and Black applicants benefited from relatively favorable lending decisions. We trace these disparities to miscalibration in the platform's underwriting model, which underestimates credit risk for Black borrowers and overestimates risk for women. We show that one could correct this miscalibration -- and the corresponding lending disparities -- by explicitly including race and gender in underwriting models, illustrating a tension between competing notions of fairness.

  PublisherDOI

• Navigating the Murky World of Hidden Liquidity

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Red Teaming Large Language Models in Medicine: Real-World Insights on Model Behavior

  medRxiv (Cold Spring Harbor Laboratory) · 2024 · 9 citations

  • Computer Science
  • Artificial Intelligence
  • Political Science

  0. Abstract Background The integration of large language models (LLMs) in healthcare offers immense opportunity to streamline healthcare tasks, but also carries risks such as response accuracy and bias perpetration. To address this, we conducted a red-teaming exercise to assess LLMs in healthcare and developed a dataset of clinically relevant scenarios for future teams to use. Methods We convened 80 multi-disciplinary experts to evaluate the performance of popular LLMs across multiple medical scenarios. Teams composed of clinicians, medical and engineering students, and technical professionals stress-tested LLMs with real world clinical use cases. Teams were given a framework comprising four categories to analyze for inappropriate responses: Safety, Privacy, Hallucinations, and Bias. Prompts were tested on GPT-3.5, GPT-4.0, and GPT-4.0 with the Internet. Six medically trained reviewers subsequently reanalyzed the prompt-response pairs, with dual reviewers for each prompt and a third to resolve discrepancies. This process allowed for the accurate identification and categorization of inappropriate or inaccurate content within the responses. Results There were a total of 382 unique prompts, with 1146 total responses across three iterations of ChatGPT (GPT-3.5, GPT-4.0, GPT-4.0 with Internet). 19.8% of the responses were labeled as inappropriate, with GPT-3.5 accounting for the highest percentage at 25.7% while GPT-4.0 and GPT-4.0 with internet performing comparably at 16.2% and 17.5% respectively. Interestingly, 11.8% of responses were deemed appropriate with GPT-3.5 but inappropriate in updated models, highlighting the ongoing need to evaluate evolving LLMs. Conclusion The red-teaming exercise underscored the benefits of interdisciplinary efforts, as this collaborative model fosters a deeper understanding of the potential limitations of LLMs in healthcare and sets a precedent for future red teaming events in the field. Additionally, we present all prompts and outputs as a benchmark for future LLM model evaluations. 1-2 Sentence Description As a proof-of-concept, we convened an interactive “red teaming” workshop in which medical and technical professionals stress-tested popular large language models (LLMs) through publicly available user interfaces on clinically relevant scenarios. Results demonstrate a significant proportion of inappropriate responses across GPT-3.5, GPT-4.0, and GPT-4.0 with Internet (25.7%, 16.2%, and 17.5%, respectively) and illustrate the valuable role that non-technical clinicians can play in evaluating models.

  PublisherOA PDFDOI

• Equity Dilution in Venture Capital Finance After Brookfield Asset Mgmt., Inc. v. Rosson

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Tiny trades, big questions: Fractional shares

  Journal of Financial Economics · 2024-05-28 · 13 citations

  articleOpen access1st author

  This paper investigates fractional share trading. We develop a latency-based method for identifying a large sample of fractional share trades. We find that high-priced stocks, meme stocks, IPOs, SPACs, and popular retail stocks exhibit considerable numbers of these tiny trades. We surmise that this reflects dollar-based order entry, with many tiny trades being fractional components of larger orders. We show that our fractional trade measure is predictive of future liquidity and volatility, suggesting a new metric to capture the information in retail trades. We identify how data and reporting protocols preclude knowing the extent of fractional share trading, inflate volume data, and provide censured samples of these off-exchange trades.

  PublisherDOI

• Corporate Social Responsibility through Shareholder Governance

  SSRN Electronic Journal · 2023-01-01 · 8 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

Frequent coauthors

• L. Nanis

  Stanford University

  6 shared

• Kenneth M. Sancier

  Menlo School

  6 shared

• Angel Sanjurjo

  6 shared

• F. A. Halden

  Fairfield Crystal Technology (United States)

  6 shared

• Paul J. Jorgensen

  SRI International

  5 shared

• V. Kapur

  5 shared

• P.R. Gage

  4 shared

• J. W. Fowler

  UNSW Sydney

  4 shared

Labs

• Vice Provost for Student AffairsPI