Year: 2024
Type: report
Abstract: Automation bias is a critical issue for artificial intelligence deployment. It can cause otherwise knowledgeable users to make crucial and even obvious errors. Organizational, technical, and educational leaders can mitigate these biases through training, design, and processes. This paper explores automation bias and ways to mitigate it through three case studies: Tesla’s autopilot incidents, aviation incidents at Boeing and Airbus, and Army and Navy air defense incidents.
DOI: 10.51593/20230057
Authors: Lauren Kahn, Emelia Probasco, Ronnie Kinoshita
Institution: Center for Security and Emerging Technology
Language: English
Subfield: Human Factors and Ergonomics
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W7160376673