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.

大数据和 AI 驱动模型的准确性和质量对于建立准确的人工智能模型至关重要。然而，在根本性数据库生成中存在一些关键障碍，这些常常在文献中被低估并且得到很少的讨论。在本文中，我们特别针对超导和热电材料进行了两个代表性案例研究，讨论了三个方面，即内在有偏采样选择，可能的隐藏变量，以及不同的数据年龄。值得注意的是，据我们所知，我们提出并测试了第一个能够检测和量化内在数据偏差的策略。