R0043/2026-03-28/Q001/SRC04
Georgetown CSET — AI Safety and Automation Bias (November 2024)
Source
| Field |
Value |
| Title |
AI Safety and Automation Bias: The Downside of... |
| Publisher |
Center for Security and Emerging Technology, Georgetown University |
| Author(s) |
Lauren Kahn, Emelia S. Probasco, Ronnie Kinoshita |
| Date |
November 2024 |
| URL |
https://cset.georgetown.edu/publication/ai-safety-and-automation-bias/ |
| Type |
Policy research brief |
Summary
| Dimension |
Rating |
| Reliability |
High |
| Relevance |
High |
| Bias: Missing data |
Low risk |
| Bias: Measurement |
Low risk |
| 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 highly regarded AI policy research center at Georgetown; peer-reviewed publication process |
| Relevance |
Directly addresses automation bias as a cross-domain phenomenon with case studies in Tesla, aviation, and military — maps terminology across sectors |
| Bias flags |
No significant bias concerns |
| Evidence ID |
Summary |
| SRC04-E01 |
Cross-domain definition of automation bias with case studies demonstrating terminology usage in automotive, aviation, and military contexts |