A comprehensive review of use cases, misuses, and potential mitigation techniques in generative artificial intelligence

Abstract

• Presents an in-depth review of generative AI use cases across domains such as healthcare, education, finance, law, and architecture. • Identifies key misuse scenarios, including misinformation, hallucination, and privacy risks associated with generative models. • Comprehensively surveys mitigation strategies and ethical alignment techniques. • Offers a unified framework comparing prior surveys and emphasizes open research challenges in trustworthy GenAI deployment. Generative Artificial Intelligence (GenAI) refers to the aspect of Artificial Intelligence (AI) that deals with generating content such as texts, images, music, videos, etc. GenAI has seen a surge in interest recently, particularly with the emergence of tools like ChatGPT, which demonstrate the potential of AI to generate contextually relevant and human-like outputs across various domains. GenAI has also enhanced existing language, image, and speech models, improving their performance on domain-specific tasks. This improvement has led to better integration in existing systems, including content creation, image generation, and enhanced decision-making processes. GenAI has facilitated personalized content delivery, provided data-driven insights, and automated complex tasks, enhancing efficiency and precision across various domains. While the advancements in GenAI have been greatly beneficial in multiple domains, there are many instances of misuse and abuse. This paper aims to provide a comprehensive review of the history of GenAI, its use cases, recent developments, downsides, and solutions to address the problem of its misuse in various sectors.

Ähnliche Arbeiten