For example, gender debiasing of word embeddings would negatively affect how accurately occupational gender statistics are reflected in these models, which is necessary information for NLP operations. Gender bias is entangled with grammatical gender information in word embeddings of languages with grammatical gender.13 Word embeddings are likely to contain more properties that we still haven’t discovered. Moreover, debiasing to remove all known social group associations would lead to word embeddings that cannot accurately represent the world, perceive language, or perform downstream applications. Instead of blindly debiasing word embeddings, raising awareness of AI’s threats to society to achieve fairness during decision-making in downstream applications would be a more informed strategy. Natural language processing focuses on understanding how people use words while artificial intelligence deals with the development of machines that act…