Regulatory Compliance Ceiling Effect Applied to Artificial Intelligence

Artificial intelligence (AI) is governed by rule learning via machine language in different formats or platforms. It has the ability to be very effective and efficient in identifying patterns from existing data that it curates. Presently it is limited in many of its responses as it continues to learn the most accurate, efficient means for answering questions. However, is it possible that what we are observing is a more general phenomenon of rule based systems which involves a ceiling effect.

The ceiling effect has been identified in regulatory compliance when data from a respective rule based system is compared to the relative quality of that same system. Could we be observing the same type of relationship in the AI platform responses? Those rule based systems are governed by a ceiling effect where responses have a limit in how effective they will be based upon their efficient response rate. For example, is the ceiling effect a more generic theory that can be applied to all rule based systems and goes beyond regulatory compliance measurement and more to the inherent structure of these systems.

Are more generative type AI systems more effective at eliminating the ceiling effect than a language based system that relies on data acquisition? It will be interesting to note the further development of AI platforms to see how this balance of effectiveness and efficiency plays out.

Please see previous posts on the regulatory compliance ceiling effect and diminishing returns as applied in human service regulatory and rule based delivery systems.