Document Type

Article

Publication Date

9-1-2021

Department

Computer Engineering and Computer Science

Abstract

As AI technologies increase in capability and ubiquity, AI accidents are becoming more common. Based on normal accident theory, high reliability theory, and open systems theory, we create a framework for understanding the risks associated with AI applications. This framework is designed to direct attention to pertinent system properties without requiring unwieldy amounts of accuracy. In addition, we also use AI safety principles to quantify the unique risks of increased intelligence and human-like qualities in AI. Together, these two fields give a more complete picture of the risks of contemporary AI. By focusing on system properties near accidents instead of seeking a root cause of accidents, we identify where attention should be paid to safety for current generation AI systems.

Original Publication Information

Williams, R.; Yampolskiy, R. Understanding and Avoiding AI Failures: A Practical Guide. Philosophies 2021, 6, 53. https://doi.org/10.3390/philosophies6030053

DOI

10.3390/philosophies6030053

ORCID

0000-0001-9637-1161

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