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Here is a video that explains this blog well. https://youtu.be/59bMh59JQDo https://youtu.be/59bMh59JQDo The unnerving part for me is this "eliminate negative a

• gowld 9y ago
  > Okay, how about we learn the truth, and then address that outside in real life Aside from the aspect you overlooked -- that our online lives are real life, and that AI models are (over)used to make decisions, not merely show us the data -- your idea is quite what the article recommends: > . 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.