One of the risks of using AI systems is the black-box nature of the results produced by the models. Why were certain decisions made? What criteria were used? What information was used to produce a result?
Explainable AI is the umbrella term used for AI systems that are capable of rationalizing their decisions in a way that is understandable for humans. In a world that is rapidly moving towards automation with AI systems, implementing explainability in our software is going to be an important step to create transparency, trust, and comply with inevitable future regulation.
To supercharge your developments in explainable AI, check out Deeploy.io, a software solution that provides functionality to host AI models with built-in explainers, providing users with easily explorable explanations for results and extensive audit trails.