It is important to consider the editing capabilities of AI systems. Editability enables the modification and/or removal of learned records from an AI’s memory.
An auditable, verifiable, and validation-ready AI system should be capable of following information backward from outputs to inputs.
XAI gives you insight into why an AI system produced a specific prediction set. Interpretable AI refers to understanding how an AI system works.
Computable AI has deterministic, efficient processes and algorithms that enable the exact calculation of response times for real-time operations.
The goal of XAI is to develop AI models that can provide clear explanations of their decision-making processes so that humans can trust and verify their outputs.
Using unexplainable deep learning models in safety-critical industries is equivalent to using tea-leaf reading to pilot a plane.
Evaluate an AI’s data, memory, and algorithmic structures individually for any given AI technology being used in safety-critical applications.
Rather than attempt to simulate the brain, a winning strategy is emulating known modular functions in an environment that allows consciousness to emerge.
