Creating return on investment for large-scale metadata creation

The scholarly communications industry is turning its attention to large-scale metadata creation for enhancing discovery of content. Algorithms used to train machine learning are powerful, but need to be used carefully. Several ethical and technological challenges need to be faced head-on to use of machine learning without exacerbating bias, racism, and discrimination. This article highlights the specific needs of humanities research to address historical bias and curtail algorithmic bias in creating metadata for machine learning. It also argues that the return on investment for large-scale metadata creation begins with building transparency into metadata creation and handling. © 2021 - The authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (CC BY-NC 4.0).