Stephen Casper (Cas)
I (Cas) work on a range of projects from technical safeguards to technical governance. This stream follows an academic collaboration model and will work will likely focus on technical topics in AI governance.
Stream overview
Technical work: Making safeguards 'run deep', including safeguards and risk management for open-weight models.
Governance work: Critical review of industry self-governance, critical review of national AI governance institutes, open-weight model governance, predicting and mitigating future AI incidents.
Mentors
Stephen (“Cas”) Casper is a final year Ph.D student at MIT in the Algorithmic Alignment Group advised by Dylan Hadfield-Menell. His work focuses on AI safeguards and technical governance. His research has been featured at NeurIPS, AAAI, Nature, FAccT, EMNLP, SaTML, TMLR, IRAIS, several course curricula, a number of workshops, and over 20 news articles and newsletters. He is also a writer for the International AI Safety Report and the Singapore Consensus. In addition to MATS, he also mentors for ERA and GovAI. In the past, he has worked closely with over 30 mentees on various safety-related research projects.
Mentorship style
2-3 meetings per week plus regular messaging and collaborative writing.
Representative papers
Here are some examples of papers related to safeguards and technical AI governance. If you are interested in any of them, you might be interested in this stream:
- Open Technical Problems in Open-Weight AI Model Risk Management
- Open Problems in Technical AI Governance
- Risk Mitigation Strategies for the Open Foundation Model Value Chain
- We Should Chart an Atlas of All the World's Models
- The 2024 Foundation Model Transparency Index
- AI-Enabled Coups: How a Small Group Could Use AI to Seize Power
- How Do AI Companies “Fine-Tune” Policy? Examining Regulatory Capture in AI Governance
- The Most Dangerous Fiction: The Rhetoric and Reality of the AI Race
- The Values Encoded in Machine Learning Research
Scholars we are looking for
Green flags include:
- Research experience and taste
- Demonstrated initiative in ideating and leading prior projects
- Demonstrated ability to 'succeed even when not set up to succeed' in past research/projects.
- Taking a critical mindset about projects and impact
- Interest in academia
This stream will follow an academic collaboration model. Scholars will be free to discuss and collaborate externally. However, scholars should also expect to work in collaboration with others in the stream.
Project selection
Mentor(s) will talk through project ideas with scholar.