Transparency and Explainability in AI
How retrain.ai is a responsible, white-box solution
The ultimate aim of Responsible AI is to reduce the risk of unintended bias. To understand how this works, and to adjust for accuracy over time, it’s critical to have transparency throughout the process.
The level of transparency needed to fully explain an AI solution can only be found in what is referred to as a white box solution. With this approach, a full end-to-end view of an AI system’s functionality enables system users to see the what of the system–its data output–while also being able to ask the why–the methodology behind the results.
As a fully explainable, Responsible AI solution, retrain.ai provides interpretability to allow data scientists and analysts to test the design and internal structure of the system to authenticate the input and outflow, gauge for errors or inconsistencies, and optimize functionalities accordingly.