  Neural nets are powerful function approximators, but the behavior of a given
neural net, once trained, cannot be easily modified. We wish, however, for
people to be able to influence neural agents' actions despite the agents never
training with humans, which we formalize as a human-assisted decision-making
problem. Inspired by prior art initially developed for model explainability, we
develop a method for updating representations in pre-trained neural nets
according to externally-specified properties. In experiments, we show how our
method may be used to improve human-agent team performance for a variety of
neural networks from image classifiers to agents in multi-agent reinforcement
learning settings.
