An algorithm is a set of directions or a formula, often for a machine to execute. We have algorithms that determine credit scores, Google search rankings, Spotify discover playlists, and even how results are ordered in library databases. Algorithms can reflect the biases of their creators and the larger world they occupy perpetuating societal biases such as racism and sexism.
In library databases we usually encounter two types of biases: content and indexing. Content bias is what sources were selected to include in the database, these are often peer-reviewed scholarly sources, part of a scholarly communication system, where many studies document racism and sexism in the peer-review process. Indexing biases occur when subjects or keywords are assigned that provide moral, social, or political commentary on the information source.
For a more detailed analysis of algorithmic biases check out Matthew Reidsma's article "Algorithmic Bias in Library Discovery Systems" and Dr. Safiya Noble's book "Algorithms of Oppression: How Search Engines Reinforce Racism as introductions to this topic.
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