Understanding Risk Associated With Artificial Intelligence for Generalized Assessment and Decision Making: A Design Science Approach
type: CreativeWork
description: Artificially Intelligent technology has great potential; however, contained within that potential are myriad threats and unintended consequences that range from minor inconveniences to global catastrophes. This research articulates a framework that can be used to quantitively evaluate existing, as well as emergent, artificially intelligent technologies from a perspective of risks associated with increased capability. Grounded in existing AI risk management and quantification methods and remedies, but using the Design Science Research methodology, we use quantitative measures to create a framework called Ordinal-W that provides a novel and intuitive way of “scoring” any risks associated with autonomous entities. This score can be used to evaluate emergent technologies, as well as to compare disparate technologies, and identify specific characteristics that may lead to potential risks prior to their introduction into mission-critical or life-threatening situations. The research identifies 24 dimensions of AI-enabled autonomous entities. These dimensions represent a set of collectively comprehensive, yet mutually exclusive, individual characteristics of AI-enabled technology for classification and scoring with an aim to finding the smallest set that still captures subtle variations in all AI-enabled technology. Each dimension is given an ordinal scale with a range of 0-9 to measure the amount of associated risk. Each dimension’s scale value is then combined to create an overall Ordinal-W Score for the entity. To account for the increased risk of AI at scale (from one low-risk entity to millions or billions of entities) the Score is logarithmic in nature with a range of 0-1000. In addition to the framework to evaluate risk, a second artifact is a web site and online app to demonstrate the framework’s use. Users can enter all the requisite information for scoring any entity. Over 100 entities were entered in the system during testing. The system allows any user to enter …
abstract: Artificially Intelligent technology has great potential; however, contained within that potential are myriad threats and unintended consequences that range from minor inconveniences to global catastrophes. This research articulates a framework that can be used to quantitively evaluate existing, as well as emergent, artificially intelligent technologies from a perspective of risks associated with increased capability. Grounded in existing AI risk management and quantification methods and remedies, but using the Design Science Research methodology, we use quantitative measures to create a framework called Ordinal-W that provides a novel and intuitive way of “scoring” any risks associated with autonomous entities. This score can be used to evaluate emergent technologies, as well as to compare disparate technologies, and identify specific characteristics that may lead to potential risks prior to their introduction into mission-critical or life-threatening situations. The research identifies 24 dimensions of AI-enabled autonomous entities. These dimensions represent a set of collectively comprehensive, yet mutually exclusive, individual characteristics of AI-enabled technology for classification and scoring with an aim to finding the smallest set that still captures subtle variations in all AI-enabled technology. Each dimension is given an ordinal scale with a range of 0-9 to measure the amount of associated risk. Each dimension’s scale value is then combined to create an overall Ordinal-W Score for the entity. To account for the increased risk of AI at scale (from one low-risk entity to millions or billions of entities) the Score is logarithmic in nature with a range of 0-1000. In addition to the framework to evaluate risk, a second artifact is a web site and online app to demonstrate the framework’s use. Users can enter all the requisite information for scoring any entity. Over 100 entities were entered in the system during testing. The system allows any user to enter …
author: Wagner, William
keywords: Assessment, Ordinal-W, Risk, Artificially intelligent technology, AI-enabled technology
date published: 2025-01-01T08:00:00Z
date modified: 2026-07-24T01:41:17Z
provider: Claremont Graduate University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Claremont Graduate University