Classification Schemas for Artificial Intelligence Failures
Peter J. Scott, Roman V. Yampolskiy
TL;DR
This paper analyzes historical AI failures, proposes a classification scheme to categorize future failures, and aims to improve responses and reduce failures through systematic risk assessments.
Contribution
It introduces a novel classification scheme for AI failures to enhance response strategies and guide risk assessments in development processes.
Findings
A systematic classification aids in response planning.
Targeted risk assessments can reduce AI failures.
Historical failure analysis informs future prevention strategies.
Abstract
In this paper we examine historical failures of artificial intelligence (AI) and propose a classification scheme for categorizing future failures. By doing so we hope that (a) the responses to future failures can be improved through applying a systematic classification that can be used to simplify the choice of response and (b) future failures can be reduced through augmenting development lifecycles with targeted risk assessments.
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