Reducing Bias in AI by Forgetting

Intriguing thought.   When we built a learning system we included the notion of forgetting as an aspect of scheduling maintenance, re-running and retesting particular solutions.    But not in terms of changes in bias.  Which I would assume would be a re-test.

How to Reduce Bias in AI? Selective Amnesia.
USC Viterbi School of Engineering
Rishbha Bhagi
February 24, 2020

Artificial intelligence (AI) researchers at the University of Southern California (USC) Viterbi School of Engineering’s Information Sciences Institute have created a mechanism for inducing selective amnesia in computing models. This adversarial forgetting methodology could help reduce bias in AI by teaching deep learning models to ignore unwanted data factors. The mechanism is used to train a neural network to represent all underlying aspects of the data being analyzed, and then to forget specified biases, resulting in models that lack those biases when making decisions. Adversarial forgetting also could enhance content generation. USC's Greg Ver Steeg said, "For content generation to succeed, we need new ways to control and manipulate neural network representations and the forgetting mechanism could be a way of doing that."  ... "