Predicting future AI failures from historic examples. Issue 1 (11th March 2019)

• Record Type: Journal Article
• Authors: Yampolskiy, Roman V.
• Is Part Of: Foresight. Volume 21:Issue 1(2019)
• Page Start: 138
• Page End: 152
• Publication Date: 2019-03-11
• Subjects: Cybersecurity -- Failures; Forecasting -- Periodicals; Policy sciences -- Periodicals; 320.05
• DOI: 10.1108/FS-04-2018-0034
• Languages: English
• ISSNs: 1463-6689

Abstract

Abstract : Purpose: The purpose of this paper is to explain to readers how intelligent systems can fail and how artificial intelligence (AI) safety is different from cybersecurity. The goal of cybersecurity is to reduce the number of successful attacks on the system; the goal of AI Safety is to make sure zero attacks succeed in bypassing the safety mechanisms. Unfortunately, such a level of performance is unachievable. Every security system will eventually fail; there is no such thing as a 100 per cent secure system. Design/methodology/approach: AI Safety can be improved based on ideas developed by cybersecurity experts. For narrow AI Safety, failures are at the same, moderate level of criticality as in cybersecurity; however, for general AI, failures have a fundamentally different impact. A single failure of a superintelligent system may cause a catastrophic event without a chance for recovery. Findings: In this paper, the authors present and analyze reported failures of artificially intelligent systems and extrapolate our analysis to future AIs. The authors suggest that both the frequency and the seriousness of future AI failures will steadily increase. Originality/value: This is a first attempt to assemble a public data set of AI failures and is extremely valuable to AI Safety researchers.