Inductive Models for Artificial Intelligence Systems are Insufficient
  without Good Explanations
Udesh Habaraduwa
This paper discusses the limitations of machine learning (ML), particularly
deep artificial neural networks (ANNs), which are effective at approximating
complex functions but often lack transparency and explanatory power. It
highlights the `problem of induction' : the philosophical issue that past
observations may not necessarily predict future events, a challenge that ML
models face when encountering new, unseen data. The paper argues for the
importance of not just making predictions but also providing good explanations,
a feature that current models often fail to deliver. It suggests that for AI to
progress, we must seek models that offer insights and explanations, not just
predictions.