Emily Hadley
Emily Hadley

Quantitative Threat Forecasting Analyst

OpenAI

About Me

I use data science to make AI safer 🤖

Interests
  • Misuse & Manipulation of AI
  • AI in High-Trust Domains
  • Early Signals of AI Risk
Education

  • MSc Analytics

    North Carolina State University

  • BS Statistics, BA Public Policy

    Duke University

What I Work On

I work on identifying, understanding, and forecasting risks related to AI.

My background spans AI red teaming, threat forecasting, and applied data science across domains including health, education, and criminal justice. I use a mix of data analysis, modeling, and experimentation to study real-world AI challenges.

Featured Publications
Investigating algorithm review boards for organizational responsible artificial intelligence governance
Prioritizing Policies for Furthering Responsible Artificial Intelligence in the United States
Recent Publications
Miles Crosskey, Tomas McIntee, Sandy Preiss, Daniel Brannock, John M Baratta, Yun Jae Yoo, Emily Hadley, Frank Blanceró, Robert Chew, Johanna Loomba, Abhishek Bhatia, Christopher G Chute, Melissa Haendel, Richard Moffitt, Emily R Pfaff (2025). Re-engineering a machine learning phenotype to adapt to the changing COVID-19 landscape - a machine learning modelling study from the N3C and RECOVER consortia. The Lancet Digital Health.
DOI URL
Chengxi Zang, Daniel Guth, Ann M. Bruno, Zhenxing Xu, Haoyang Li, Nariman Ammar, Robert Chew, Nick Guthe, Emily Hadley, Rainu Kaushal, Tanzy Love, Brenda M. McGrath, Rena C. Patel, Elizabeth C. Seibert, Yalini Senathirajah, Sharad Kumar Singh, Fei Wang, Mark G. Weiner, Kenneth J. Wilkins, Yiye Zhang, Torri D. Metz, Elaine Hill, Thomas W. Carton (2025). Long COVID after SARS-CoV-2 during pregnancy in the United States. Nature Communications.
DOI URL
Lee Hoffer, Allison v. Schlosser, Emily Hadley, Georgiy Bobashev (2024). The Frequency and Characteristics of Brokered Illegal Drug Sales: Reconceptualizing Illegal Drug Markets and Measuring Local Demand. Contemporary Drug Problems.
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Emily Hadley, Alan Blatecky, Megan Comfort (2024). Investigating algorithm review boards for organizational responsible artificial intelligence governance. AI and Ethics.
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Chengxi Zang, Daniel Guth, Ann Bruno, Zhenxing Xu, Nariman Ammar, Robert Chew, Nicholas Güthe, Emily Hadley, Rainu Kaushal, Tanzy Love, Brenda McGrath, Rena Patel, Elizabeth Seibert, Sharad Singh, Fei Wang, Mark Weiner, Kenneth Wilkins, Yiye Zhang, Torri Metz, Elaine Hill, Thomas Carton (2024). Post-Acute Sequelae of SARS-CoV-2 Infection in Pregnant Females: An Electronic Health Records Analysis from the RECOVER Initiative (PCORnet and N3C). Research Square.
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Speaking

I have presented or spoken at more than 35 conferences, including several as an invited speaker or panelist. Examples include:

  • IEEE Big Data
  • ACM Conference on Fairness, Accountability, and Transparency (FAccT)
  • ASA Joint Statistical Meetings
  • Academy Health Annual Research Meeting (ARM)
  • Government Advances in Statistical Programming (GASP)
News

GPT-5 System Card

Contributed to work by the Microsoft AI Red Team cited as important to this release.

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