Sophisticated models trained on biased data can learn discriminatory patterns, which leads to skewed decisions. A new solution aims to prevent neural networks from making decisions based on common biases.
What’s new: Ehsan Adeli and a group at Stanford propose Bias-Resilient Neural Network, or BR-Net, an architecture that works with a classifier to minimize the impact of biases that are well understood. In the training data, we can label, say, race and gender (known as bias variables), and BR-Net will learn to prevent spurious correlations between those variables and the model's output classification.
Key insight: Biases in data correlate with class labels. If one part of a network learns to predict this correlation, another can learn to minimize the predicted correlation. This adversarial scheme can mitigate bias.
How it works: BR-Net comprises three neural networks. The feature extractor finds embeddings of input data. The classifier predicts class labels from the embeddings. The bias predictor predicts the correlation between embeddings and bias variables. Once labels for bias variables have been added to the data, training proceeds in three steps:
- First, the system maximizes classification accuracy: The feature extractor and classifier together learn to predict labels.
- Then it identifies the effects of bias variables: The bias predictor learns the correlation between embeddings and bias variables.
- Finally, it minimizes the influence of bias: The feature extractor learns to generate embeddings that don’t correlate with the bias variables’ labels.
- By iterating through these steps, the feature extractor generates embeddings that maximize the classifier’s performance and minimize the biased correlation between embeddings and labels.
Results: The researchers used a VGG16 classifier with BR-Net to predict a person’s gender from a photo. They trained the model on the GS-PPB dataset. Because classifiers often perform poorly on darker faces, they labeled skin tone as a bias variable. BR-Net achieved 96.1 percent balanced accuracy (accuracy for each of six skin tones considered equally), an improvement of 2 percent. This indicates more consistent results across different skin colors than a VGG16 trained without BR-Net.
Why it matters: Bias in AI is insidious and difficult to prevent. BR-Net offers a solution when sources of bias are known.
We're thinking: Machine learning presents hard questions to society: Which biases should we avoid? How can we come to agreement about which to avoid? Who gets to decide in the end? In lieu of answers, the choices are in the hands of ML engineers.