The Transformation Risk-Benefit Model of Artificial Intelligence: Balancing Risks and Benefits Through Practical Solutions and Use Cases

(2) Department of Management; Alabama; Clayton State University; Diane Fulton (2); Georgia; Minneapolis; Minnesota); Morrow; Nate Hayes (3); Richard Fulton (1)

The Transformation Risk-Benefit Model of Artificial Intelligence: Balancing Risks and Benefits Through Practical Solutions and Use Cases

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arxiv 2406.11863 v1 pith:UQRWINCX submitted 2024-04-11 cs.CY cs.AI

Richard Fulton (1) , Diane Fulton (2) , Nate Hayes (3) , Susan Kaplan (3) ((1) Department of Computer Science , Troy University , Troy , Alabama , USA

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(2) Department of Management Clayton State University Morrow Georgia USA (3) Modal Technologies Minneapolis Minnesota)

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classification cs.CYcs.AI

keywords modelartificialintelligencerisksbenefitscasesmodelspractical

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This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with theories and models reviewed and expanded constructs, the writers propose a new framework called "The Transformation Risk-Benefit Model of Artificial Intelligence" to address the increasing fears and levels of AI risk. Using the model characteristics, the article emphasizes practical and innovative solutions where benefits outweigh risks and three use cases in healthcare, climate change/environment and cyber security to illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational model.

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