R0044/2026-04-01/Q001/SRC01

Research	R0044 — Expanded Vocabulary Research
Run	2026-04-01
Query	Q001
Search	S01
Result	S01-R01
Source	SRC01

CSET Georgetown — AI Safety and Automation Bias Issue Brief

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 / Issue 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 respected policy research center at Georgetown University with a track record of rigorous AI policy analysis. The brief uses a three-tiered framework (user, technical, organizational) grounded in existing literature.
Relevance	Directly addresses automation bias in AI systems using the exact vocabulary from the research query. Provides case studies and a framework that distinguishes between user-side and technical/system-side factors.
Bias flags	No significant bias flags. CSET is a nonpartisan research center funded by Open Philanthropy.

Evidence Extracts

Evidence ID	Summary
SRC01-E01	Three-tiered framework for automation bias: user, technical design, and organizational factors