From robot surgery to self-driving cars, the algorithms used to control socio-technical systems are becoming more powerful.
And in the era of machine-learning, our cyber decision-makers both learn and adapt.
But what do we know about the risks involved? If machine-learning is not to be a technological black box, then there needs to be transparency and interpretability in the algorithmic designs for decision-making systems.
Fintegral will be amongst the academics, scientists and entrepreneurs discussing risk in the machine-learning age at ETH Zurich on January 20.