On some elusive aspects of databases hindering AI based discovery: A
  case study on superconducting materials
Giovanni Trezza Eliodoro Chiavazzo
It stands to reason that the amount and the quality of big data is of key
importance for setting up accurate AI-driven models. Nonetheless, we believe
there are still critical roadblocks in the inherent generation of databases,
that are often underestimated and poorly discussed in the literature. In our
view, such issues can seriously hinder the AI-based discovery process, even
when high quality, sufficiently large and highly reputable data sources are
available. Here, considering superconducting and thermoelectric materials as
two representative case studies, we specifically discuss three aspects, namely
intrinsically biased sample selection, possible hidden variables, disparate
data age. Importantly, to our knowledge, we suggest and test a first strategy
capable of detecting and quantifying the presence of the intrinsic data bias.