Improving Fairness via Intrinsic Plasticity in Echo State Networks

Artificial Intelligence, and in particular Machine Learning, has become ubiquitous in today's society, both revolutionizing and impacting society as a whole. However, it can also lead to algorithmic bias and unfair results, especially when sensitive information is involved. This paper addresses the problem of algorithmic fairness in Machine Learning for temporal data, focusing on ensuring that sensitive time-dependent information does not unfairly influence the outcome of a classifier. In particular, we focus on a class of training-efficient recurrent neural models called Echo State Networks, and show, for the first time, how to leverage local unsupervised adaptation of the internal dynamics in order to build fairer classifiers. Experimental results on real-world problems from physiological sensor data demonstrate the potential of the proposal.