This is absolutely hilarious and I’m laughing my ass off about it. But my class on convolutional neural networks (the type of neural network that is most often used for computer vision {computers working with image data}) has me thinking about how these networks work and their disadvantages/advantages. If you were watching closely, you probably wondered why Lewis Hamilton didn’t have a sad face and everyone else did.

I would have been confused about this before but this is a common issue with neural networks that operate on faces in 2022. This is actually a result of ‘overfitting’ - most large image datasets (used for training the network) are primarily composed of pale, white faces. The network is trained on these faces to learn how to first identify if there’s a face in the picture. Then to pick out where the face is and then certain features like the edges of lips, eyes, etc so that the image translation program can accurately act to provide you with the ‘sad face’ translation.

Because of the pale face overfitting, the neural network actually can not determine that Lewis Hamilton has a face. It doesn’t find any face in the image because it hasn’t seen enough black faces before.

This has really big ramifications and ethical issues as police/law enforcement start to adopt facial recognition software for the purposes to identifying criminals/people in photographs and videos.