An artificial neural network dreams up new faces whilst it’s training on a well-known dataset of thousands of celebrities. Every face seen here is fictional, imagined by the neural network based on what it’s seeing and learning. But what is it learning?
The politics of this dataset, who’s in it, who isn’t in it, how it’s collected, how it’s used and the consequences of this, is in itself a crucial topic, but not the subject of this work.
Indeed the dataset may not be representative of the diversity of the wider population. However, the learning algorithm itself provides a further layer of homogenisation. In these images produced by the neural network, whatever diversity was present in the dataset is mostly lost. All variety in shape, detail, texture, individuality and blemishes are all erased. They are smoothed out with the most common attributes dominating the results.
The network is learning an idealised sense of hyper-real beauty, a race of ‘perfect’, homogeneous specimens.
This is not a behaviour that is explicitly programmed in, it is an inherent property of the learning algorithm used to train the neural network. And while there are recent algorithms which specialise in generating more photo-realistic images (especially faces), the algorithm used here is one of the most widespread algorithms used in Machine Learning and Statistical Inference — Maximum Likelihood Estimation (MLE).
However, MLE is unable to deal with uncertainty. It’s unable to deal with the possibility that a less likely, a less common hypothesis might actually be valid.
MLE is binary. It has no room for alternative hypotheses. Instead, the hypothesis with the highest apparent likelihood is assumed to be unequivocally true.
MLE commits to this dominant truth, and everything else is irrelevant, incorrect and ignored. Any variations, outliers, or blemishes are erased; they’re blurred and bent to conform to this one dominant truth, this binary world view.
Perhaps not unlike the increasingly polarising and binary discourse in our increasingly divided societies.