The superior functionality and versatility of generative AI have raised expectations for the improvement of human society and concerns about the ethical and social risks associated with the use of generative AI. Many previous studies have presented risk issues as concerns associated with the use of generative AI, but since most of these concerns are from the user's perspective, they are difficult to lead to specific countermeasures. In this study, the risk issues presented by the previous studies were broken down into more detailed elements, and risk factors and impacts were identified. In this way, we presented information that leads to countermeasure proposals for generative AI risks.

Recently, various risks have been pointed out in artificial intelligence (AI) systems. In particular, AI security, AI ethics, and AI quality are considerable risks. To make AI systems trustworthy against these risks, risk assessment technology is needed to identify potential AI risks and decide which risks should be dealt with in priority. We propose a risk assessment technology that assesses three kinds of risks—AI security, AI ethics, and AI quality—which have been considered separately. Our technology follows the ISO 31000 framework, consisting of four phases: risk candidate identification, impact assessment, likelihood assessment, and priority derivation for countermeasures. To realize this technology, risk candidate identification is achieved by extending AI ethics impact assessment— an identification method of AI ethics risk—to AI security and AI quality. Impact and likelihood assessments are conducted by extending assessment methods for AI security to AI ethics and AI quality. We conducted a case study using our technology and confirmed that the risks were appropriately extracted, and the priority of the risks to be dealt with was derived.

We propose a new security risk assessment approach for Machine Learning-based AI systems (ML systems). The assessment of security risks of ML systems requires expertise in ML security. So, ML system developers, who may not know much about ML security, cannot assess the security risks of their systems. By using our approach, a ML system developers can easily assess the security risks of the ML system. In performing the assessment, the ML system developer only has to answer the yes/no questions about the specification of the ML system. In our trial, we confirmed that our approach works correctly.

This paper proposes a new algorithm for evaluating the number of chaining variable conditions(CVCs) in the selecting step of a distrubance vector (DV) for the analysis of SHA-1 collision attack. The algorithm is constructed by combining the following four strategies, Strict Bit Compression, DV expansion, Precise Counting Rules in Every Step and Differential Path Confirmation for Rounds 2 to 4, that can evaluate the number of CVCs morestrictly compared with the previous approach.