[zaphos]

The article does not advocate tuning out the results; it is advocating awareness of the results and careful consideration of how to proceed. See the detailing of options at the end of the second case study:

>> "As with Tia, Tamera has several choices she can make. She could simply accept these biases as is and do nothing, though at least now she won't be caught off-guard if users complain. She could make changes in the user interface, for example by having it present two gendered responses instead of just one, though she might not want to do that if the input message has a gendered pronoun (e.g., "Will she be there today?"). She could try retraining the embedding model using a bias mitigation technique (e.g., as in Bolukbasi et al.) and examining how this affects downstream performance, or she might mitigate bias in the classifier directly when training her classifier (e.g., as in Dixon et al. [1], Beutel et al. [10], or Zhang et al. [11]). No matter what she decides to do, it's important that Tamera has done this type of analysis so that she's aware of what her product does and can make informed decisions."