Latent-space misalignment measure of responsible ai for machine learning models

A computer-implemented method for providing improved insights about misalignment in latent space of a machine learning model, the method comprising:

• initializing, by one or more programmable processors, a second weight matrix of a second artificial neural network based on a first weight matrix from a first artificial neural network
• applying, by the one or more programmable processors, transfer learning between the first artificial neural network and the second artificial neural network, the first artificial neural network including first hidden nodes defining a first latent space from an optimized first weight matrix, the second artificial neural network including transfer learned second hidden nodes defining a second latent space and a second transfer learned weight matrix;
• comparing, by the one or more programmable processors, the first latent space with the second latent space to determine a statistical distance measurement between the first latent space and the second latent space;
• determining, by the one or more programmable processors and responsive to the comparing, a first score indicating alignment of the first latent space and the second latent space;
• and
• determining, by the one or more programmable processors and responsive to the first score satisfying a threshold, an appropriateness of the machine learning model.