PROGRAMMING WITH THE HELP OF GENERATIVE ARTIFICIAL INTELLIGENCE SYSTEMS: RISKS AND CHALLENGES
DOI:
https://doi.org/10.32689/maup.it.2023.2.2Keywords:
artificial intelligence, programming, risk, generative artificial intelligence system, machine learningAbstract
The article discusses the risks associated with the use of generative artificial intelligence (GenAI) systems. The authors emphasize that countries with technologically advanced legal systems, such as Italy and Switzerland, already regulate the use of GenAI in terms of data protection and cybersecurity. They also mention a project on the administration of generative services in China, which emphasizes the responsibility of GenAI service providers for the security and accuracy of the generated content. The authors go on to discuss the risks associated with software and IT product development, in particular the use of LLMAP (Large Language Models for Application Programming). The proposed classification of risks distinguishes between passive risks arising from working with GenAI and active risks associated with deliberate misuse. The authors point out the need for a conscious approach to the use of GenAI and the development of appropriate control and security measures. This paper contradicts advertising claims about generative systems (GenAI) and points out their potential incompleteness and unpredictability of code quality. The authors emphasize the need to take into account the passive and active risks associated with the use of such systems. Passive risks include the possibility of errors and hallucinations in the issuance of GenAI, problems with the generation of complex code, and uncontrolled dissemination of the results of their work. Active risks include the possibility of reverse engineering databases, hacking the system, and obtaining "forbidden" data. The authors recommend strict control over the use of GenAI in critical industries that require uninterrupted operation and a low probability of errors. The authors also point out the need to improve technical, organizational and legislative measures for the effective use of GenAI, such as database quality control, open access to source codes and the development of audit and control systems based on progress.
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