What is a possible consequence of AI models that are not continually monitored for accuracy?

A consequence of AI models that are not continually monitored for accuracy is a false sense of security. When organizations deploy AI models, they often rely on the initial performance metrics and assume that these models will continue to perform accurately over time. However, without regular monitoring, the models may deviate from their intended function due to changes in the data landscape, shifts in user behavior, or various other unforeseen factors. This can lead stakeholders to mistakenly believe that the AI system is performing correctly, resulting in poor decision-making based on erroneous outputs.

Continual monitoring helps to ensure that any inaccuracies are identified and addressed promptly, thus maintaining the reliability and trustworthiness of the AI systems. Ignoring this important aspect can have far-reaching implications for an organization, potentially affecting decisions, strategy, and overall operational effectiveness. In contrast to the other choices, which either do not directly relate to monitoring or imply enhancements, the possibility of creating a false sense of security is a significant risk that arises specifically from a lack of ongoing scrutiny.