I used CNN Explainer to follow two images through a small convolutional neural network: an espresso, which matches one of the model's trained classes, and a sunflower, which does not. Comparing them exposed a basic limitation of image classifiers: a model can only choose from the labels and examples represented in its dataset.
My CNN object-recognition notebook covers the model setup, training results, a test prediction and the limitations of using CIFAR-10 for image classification.
Wall's article made me think about facial recognition less as a model-accuracy problem and more as a deployment problem: what happens when an organisation treats a CNN score as objective evidence? A convolutional neural network can return a probability or similarity score, but that score does not, by itself, establish a person’s identity. In a photo app, a wrong match can merely be irritating; but in policing, border control, or military use, it can expose someone to surveillance, exclusion, arrest, or harm (Wall, 2019).