Explainability is the first step in implementing ethical, unbiased AI systems that can be employed in ways that are beneficial to society.

Real-time, computable AI can be used across industries to provide quick and accurate insights and responses in high-pressure and time-sensitive situations, improving decision-making and transforming operations.

In much of the literature, interpretability seems ancillary to explainability, often used as an additional descriptor or secondary component. But they are equally important for trust and transparency and the development of responsible AI solutions.

Traceability allows us to understand how an AI system is making decisions, identify where errors or biases appear, and ensure accountability and transparency.

As AI regulations continue to develop worldwide, it is crucial to consider the editing capabilities of AI systems as part of the analysis process.