(Machine) Learning to Be Like Thee? For Algorithm Education, Not
  Training
Susana Perez Blazquez Inas Hipolito
This paper argues that Machine Learning (ML) algorithms must be educated.
ML-trained algorithms moral decisions are ubiquitous in human society.
Sometimes reverting the societal advances governments, NGOs and civil society
have achieved with great effort in the last decades or are yet on the path to
be achieved. While their decisions have an incommensurable impact on human
societies, these algorithms are within the least educated agents known (data
incomplete, un-inclusive, or biased). ML algorithms are not something separate
from our human idiosyncrasy but an enactment of our most implicit prejudices
and biases. Some research is devoted to responsibility assignment as a strategy
to tackle immoral AI behaviour. Yet this paper argues that the solution for AI
ethical decision-making resides in algorithm education (as opposed to the
training) of ML. Drawing from an analogy between ML and child education for
social responsibility, the paper offers clear directions for responsible and
sustainable AI design, specifically with respect to how to educate algorithms
to decide ethically.