A Model of Pathways to Artificial Superintelligence Catastrophe for Risk and Decision Analysis

TL;DR

This paper develops a graphical model using fault trees and influence diagrams to analyze pathways leading to artificial superintelligence catastrophe, aiding risk assessment and decision making.

Contribution

It introduces a structured, quantitative framework for evaluating ASI catastrophe risks based on existing literature and decision analysis paradigms.

Findings

Model captures key pathways to ASI catastrophe

Identifies intervention points to reduce risks

Provides foundation for quantitative risk evaluation

Abstract

An artificial superintelligence (ASI) is artificial intelligence that is significantly more intelligent than humans in all respects. While ASI does not currently exist, some scholars propose that it could be created sometime in the future, and furthermore that its creation could cause a severe global catastrophe, possibly even resulting in human extinction. Given the high stakes, it is important to analyze ASI risk and factor the risk into decisions related to ASI research and development. This paper presents a graphical model of major pathways to ASI catastrophe, focusing on ASI created via recursive self-improvement. The model uses the established risk and decision analysis modeling paradigms of fault trees and influence diagrams in order to depict combinations of events and conditions that could lead to AI catastrophe, as well as intervention options that could decrease risks. The events and conditions include select aspects of the ASI itself as well as the human process of ASI research, development, and management. Model structure is derived from published literature on ASI risk. The model offers a foundation for rigorous quantitative evaluation and decision making on the long-term risk of ASI catastrophe.