As AI regulations continue to develop worldwide, it is crucial to consider the editing capabilities of AI systems as part of the analysis process. 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 prediction to the original trained record(s) that produced that prediction.

Whereas XAI gives you insight into why an AI system produced a specific prediction set, interpretability refers to the ability of humans to understand how an AI system works.

Artificial intelligence systems employed for safety and mission-critical decision support are not useful if results arrive after the time of need, regardless of the quality of outputs. Computable artificial intelligence is AI designed with deterministic, efficient processes and algorithms that enable the exact calculation of response times from query to predictions for real-time operations.

Explainable Artificial Intelligence (XAI) refers to artificial intelligence (AI) systems that are transparent and understandable to humans. 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.

Users should have clear guidelines when evaluating any given AI technology especially for use in safety-critical applications that can affect life, death, and well-being. Decision-makers should be able to evaluate an AI’s data, memory, and algorithmic structures individually.

Considerations for building an AI/ML system.