STAU Summer Fellowship for Public Sector AI Governance (PS-AIG)

During the grant period, RegLab organized and executed a 10-week Summer Fellowship for Public Sector AI Governance (PS-AIG). Through the PS-AIG program, fellows participated in regular RegLab meetings and were embedded in RegLab research teams that provided them with the opportunity to develop hands-on experience with innovative, high impact research that directly informs federal agencies and policy-makers. A key feature of the PS-AIG program is that fellows were treated as research partners within the lab and were provided with the intensive mentorship necessary for them to succeed in the program and in their future careers. Thirteen fellows participated in the PS-AIG program, nine of which were directly supported by grant funds. From January through May 2022, RegLab solicited and reviewed applications and selected the PS-AIG Fellows. Student interest in the program was exceptional and we were only able to offer fellowships to about seven percent of applicants. The cohort of fellows hailed from six institutions and included five PhD students in computer science, environmental resources, statistics, political science, and public policy; four MS students in computer science, symbolic systems, public policy, and earth systems; one law student with an interest in AI audits; and two undergraduates in computer science. Most fellows began the PS-AIG program at the beginning of June and completed the program in mid-August, but academic schedules required some fellows to begin later and the formal program ran through mid-September. RegLab continued to provide mentorship to PS-AIG Fellows throughout the duration of the grant period, especially with those fellows who co-authored publications with RegLab team members and partners that resulted from the research they participated in as PS-AIG Fellows.
Additional Public Access To Materials:
https://github.com/reglab/redundant-encodings
Date:
2023-07-01
Primary Material Type:
Report
Other Material Types:
Guide, Model, Registered Apprenticeship, Report, Workshop and Training Material
Institution:
Stanford University
Funding Source:
Network Challenge Grant TAACCCT Round 3
Subjects:
AI, Artificial Intelligence, PIT, public interest technology, Governance, Data & Algorithms

Industry / Occupation

Industry Sector:
Public Interest Technology -- Data -- Algorithms
Occupation:
Management Occupations -- Computer and Information Systems Managers (11-3021)

Education / Instructional Information

Instructional Program:
Computer and Information Sciences and Support Services (11)
Credit Type:
  • Credit
Credential Type:
  • Bachelors Degree
Educational Level of Materials:
  • 2nd Year Community College or equivalent
  • Upper division of Bachelors degree or equivalent
Time Required:
academic year
Language:
English (United States)
Quality of Subject Matter was assured by:
  • Participation as an ongoing member of team developing the instructional materials
Quality of Online/Hybrid Course Design assured by:
  • None
Course Note:
PS AIG Program

Copyright / Licensing