In order to push the envelope, we realized we needed a sensible way to examine AI techniques and technologies. We came up with ExCITE. Now, we’re helping you evaluate AI for your use cases. ExCITE is a technology-agnostic evaluation methodology focused on transparency in Artificial Intelligence solutions.Learn More
that raises the expectations of AI solutions to meet regulatory standards and encompasses many of the elements an AI needs to operate transparently and effectively in applications that affect life, death, and well-being.
why a prediction was made.
Our universal input format converts user data into sequences of symbols before feeding into the system. All information flowing through GAIuS is presented in human terms so users can see what is happening and why.
how long it will take to produce predictions.
Our framework is deterministic, meaning same inputs = same outputs. Therefore, predictions are returned in a deterministic amount of time for RTOS.
how data filters through
Each algorithm and each datum can be reviewed before and after it is transformed by additional algorithms. Given that the system is deterministic, it is possible to predict specific outcomes based on adjustments in input data or parameters within the system.
predictions back to
Every learned record provided for training remains intact (i.e., not modeled) and is given a unique ID. When a prediction ensemble is returned, each prediction object contains the unique ID of the learned record the system pattern matched on to produce that prediction. A user/app can track these IDs against their application’s unique IDs to show exact training records.
source data for refinements and correction.
If a user spots a problem or something missing from the original data based on a prediction provided by GAIuS they can drill down into the sequence to make real-time, human-in-the-loop refinements. Because we do not model until prediction time, users are able to edit input data sequences without any need for retraining.