Many companies rush to operationalize AI models that are neither understood nor auditable in the race to build predictive models as quickly as possible with open source tools that many users don't fully understand. In my data science organization, we use two techniques -- blockchain and explainable latent features -- that dramatically improve the explainability of the AI models we build. In 2018 I produced a patent application (16/128,359 USA) around using blockchain to ensure that all of the decisions made about a machine learning model, a fundamental component of many AI solutions, are recorded and auditable. My patent describes how to codify analytic and machine learning model development using blockchain technology to associate a chain of entities, work tasks and requirements with a model, including testing and validation checks. The blockchain substantiate a trail of decision-making.

Explainability issues arise because machine learning outputs are numerical; deep neural networks are so opaque that users don't necessarily know which factor contributed to what aspect of the resulting score. There are several emergent techniques for increasing explainability and interpretability of machine learning results. After organizations gain insight into the black box of intricate machine learning models, the best way to explain those results to customers, regulators and legal entities is to translate them into rules that, by their very definition, offer full transparency for explainable AI. Rules can also highlight points of bias in models.