Fixing Bias in AI Systems by Building Better AI Models

AI models are as good as the algorithms and data they are trained on. When an AI system fails, it is usually due to three factors; 1) the algorithm has been incorrectly trained, 2) there is bias in the system's training data, or 3) there is developer bias in the model building process. The focus of this article is on the bias in training data and the bias that is coded directly into AI systems by model developers. "I think today, the AI community at large has a self-selecting bias simply because the people who are building such systems are still largely white, young and male. I think there is a recognition that we need to get beyond it, but the reality is that we haven't necessarily done so yet."