Understanding Risk Associated With Artificial Intelligence for Generalized Assessment and Decision Making: A Design Science Approach

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 values for the twenty-four dimensions. It guides the user, providing descriptions and use cases along the way. Anyone can add an entity to the system and receive feedback about which of the twenty-four dimensions present greater risk, as well as how much risk is associated with any dimension when compared to others. This allows users of all levels of technical expertise to understand risks associated with AI that may otherwise have gone unnoticed. And it allows stakeholders at all phases of a development cycle to allocate research and other resources to proactively mitigate those risks. The Ordinal-W framework, as presented herein has been shown to facilitate conversations about the very complicated topic of AI risk management and mitigation. By necessity, a person needs an understanding of many factors when trying to evaluate the risks posed by individual AI entities. The dimensions and scales used in the framework create a frame of reference for users of all disciplines and technical capabilities. This frame of reference guides the user to deliberately think through how the various dimensions affect their entity. In the process, the system may introduce ideas or risks about which the user may not have considered. Also, results may suggest when a corporate or governmental agency needs to recalibrate their method of identifying or mitigating specific threats. While the system does not set a threshold as to when a given aspect of an entity becomes too risky, it does provide quantitative evidence of a condition. In the same way as two doctors may agree or disagree on a course of treatment after seeing an MRI image, this system cannot prescribe a recommended treatment, but it should provide concrete ideas and values with which to make informed decisions. Our research demonstrates that the more experience a person has with AI at home, in the office, or for medical purposes, the more they have concerns about its use. This research seeks to move the discussions of AI risk in a proactive direction. Private organizations as well as political groups can and should increase transparency and trust by addressing these concerns and explaining to consumers how their product or regulation mitigates the risks. This framework provides a means of calibrating those expectations for all stakeholders. All AI researchers, developers, proponents, and regulators have a vested interest in reversing the current trend of increased AI use correlating to increased concern.

Ähnliche Arbeiten