Toward a Trustworthy Artificial Intelligence System Considering Security, Ethics, and Quality

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.