R0044/2026-03-29/Q003/SRC01
CSET: "AI Safety and Automation Bias" (November 2024)
Source
| Field |
Value |
| Title |
AI Safety and Automation Bias |
| Publisher |
Center for Security and Emerging Technology (CSET), Georgetown University |
| Author(s) |
Lauren Kahn, Emelia S. Probasco, Ronnie Kinoshita |
| Date |
November 20, 2024 |
| URL |
https://cset.georgetown.edu/publication/ai-safety-and-automation-bias/ |
| Type |
Policy research paper |
Summary
| Dimension |
Rating |
| Reliability |
Medium-High |
| Relevance |
High |
| Bias: Missing data |
Some concerns |
| Bias: Measurement |
N/A |
| Bias: Selective reporting |
Low risk |
| Bias: Randomization |
N/A — not an RCT |
| Bias: Protocol deviation |
N/A — not an RCT |
| Bias: COI/Funding |
Low risk |
Rationale
| Dimension |
Rationale |
| Reliability |
CSET is a respected policy research center at Georgetown. The authors are established in the AI governance space. |
| Relevance |
By title, this is the most directly relevant source — it explicitly combines "AI Safety" and "Automation Bias." Full text was not accessible to verify the depth of bridging. |
| Bias flags |
PDF could not be extracted. Analysis based on publication metadata and descriptions. |
| Evidence ID |
Summary |
| SRC01-E01 |
CSET paper combines AI safety and automation bias vocabularies in a security and emerging technology context |