Raw LLM Responses

To quote the study: “As evidenced through these services’ abilities to only recognize “male” and “female,” they also have the inability to recognize whether someone is transgender, binary or not. So, while some users expressed pride in their identities, the systems are unable to affirm this. For example was of an #agender person wearing a t-shirt that read “not cis.” The underlying infrastructure would not have the ability to recognize trans from this declaration.” It seems there is misunderstanding of what FACIAL RECOGNITION SOFTWARE is designed to do. It is not designed to read shirts. Also here: “These systems lacked the ability to contextualize implicit and explicit visual markers of gender identity, particularly in the context of trans images. For example, Microsoft misclassified an image of a bearded #transman holding up a syringe, presumably for testosterone injection based on the Instagram caption which read: “I’m back on T (testosterone) after months so hopefully I’ll be back to myself.” This is considered a definitive marker of hormone replacement therapy and trans identity, and includes “insider” markers contextual to trans communities.” Did ever even receive the caption as data? How did it receive it? You are asking a piece of software designed to recognize faces to read and understand written captions it may or may not even have access to. You are asking it to recognize and contextualize inanimate objects. If it did this and used it to draw conclusions IT WOULD NOT BE FACIAL RECOGNITION SOFTWARE.