Since the publication of the Mission Innovation Materials Acceleration Platform, AI is increasingly responsible for driving automated experimental and computational tools. There have been multiple case studies for which autonomy was demonstrated to successfully drive materials optimization or discovery problem and the world of scientific robots has moved from science fiction to reality. However, within the broader AI community it is well known that AI’s carry with them their creators’ biases and this has serious implications on model development and deployment. Using several case studies, I will illustrate how biases can arise in materials science and specific steps that can be taken to remove them. Specifically, I will discuss some of our recent work in (1) reducing human bias in label generation by applying robust statistics to spectroscopic data analysis, (2) identifying and mitigating search space bias through model disagreement, and (3) circumventing the big data bias loop by illustrating how to the presence of information redundancy in large computational datasets and (4) how construct an optimally informative dataset for model training.