AI MODEL OPTIMIZATION

Optimize Source Content Using AI Model Diagnostics

Analyze validation data to understand neural network weights and biases to address AI ethical concerns and support trust.

Evaluate and Modify Training Content

The topic of AI ethics with concerns of bias in neural networks has prompted the call for effective AI governance.

Accurate model diagnostics that support AI accountability within a larger AI governance framework will enable organizations to:

Remove protected, erroneous, or low-performing content effects
Detect performance degradation
Identify unwanted anomalies based on set thresholds and alerts
Pinpoint corresponding input stimulus and source training content within the data set
Mitigate unwanted effects or output results by removal or modification from the AI model
Continuously optimize and improve results

“Now we have the ability to correct past mistakes or the inclusion of unwarranted, undesirable, or irrelevant content effects from the AI without expensive and time-consuming removal of all subsequent training content.”

John E. Derrick

CEO, Authentrics.ai