What is the term for the possible mistrust generated by AI systems producing inaccurate information?

The term that best describes the possible mistrust generated by AI systems producing inaccurate information is "LLM Credibility Issues." This term specifically addresses the concerns surrounding large language models (LLMs) and their ability to provide reliable and factually correct information. When these models generate outputs that are inaccurate or misleading, it results in a lack of confidence among users regarding the information being presented. Such issues are crucial to acknowledge, as they directly influence public perception and acceptance of AI technologies.

Trustworthiness, while related to the concept, refers to the broader notion of reliability and ethical behavior in systems, not specifically the challenges posed by inaccurate information. Transparency pertains to how openly an AI system operates and communicates its processes and limitations, which can mitigate some credibility issues but does not directly refer to the mistrust resulting from inaccuracies. Consent involves ethical considerations related to user permission and data usage but does not speak to the concerns of the information reliability itself. Therefore, "LLM Credibility Issues" is the most accurate term for describing the mistrust stemming from AI inaccuracy.