We discuss our insights into interpretable artificial-intelligence (AI) models, and how they are essential in the context of developing ethical AI systems, as well as data-driven solutions compliant with the Sustainable Development Goals (SDGs). We highlight the potential of extracting truly-interpretable models from deep-learning methods, for instance via symbolic models obtained through inductive biases, to ensure a sustainable development of AI.
@article{arxiv.2108.10744,
title = {Interpretable deep-learning models to help achieve the Sustainable Development Goals},
author = {Ricardo Vinuesa and Beril Sirmacek},
journal= {arXiv preprint arXiv:2108.10744},
year = {2021}
}