Sketchy recognition 2020
Ricoeur's course of recognition, from recognising to being recognised.
The choice of source material for a machine learning model carries many implications.
When we use a model, the model trains us. Thus when the model detects a skyscraper, one learns how to make one's drawing even more a skyscraper...
When we force the model to mis-recognise, by showing the software something far from the examples it's been trained with, it allows us to recognize the particularities of the model.
Addressing the model
In some cases, drawers could be seen to follow the intepretations given by the computer vision model. In one instance, the (mis) intepretation of her drawing of a bear as a cactus, led the drawer to add spikes in response. In another case, the drawer replied to the systems interpretation of her drawing as a "skyscraper", by having her drawn figure announce, "I am a skyscraper on the inside". Some reponses were more confrontational, as the drawer (in this case someone familiar with machine learning models) responded to the interpretation of her drawing in progress as possibly a "fire-hydrant" by directly addressing the model writing on her drawing "you are US-centric".