3 kinds of bias in AI models -- and how we can address them

Automated decision-making tools are becoming increasingly ubiquitous in our world. As ML models become more widely adopted, special care and expertise are needed to ensure that artificial intelligence (AI) improves the bottom line fairly. ML models should target and eliminate biases rather than exacerbate discrimination. But in order to build fair AI models, we must first build better methods to identify the root causes of bias in AI. We must understand how a biased AI model learns a biased relationship between its inputs and outputs.