Tools Tackle AI's Bias, Trust Problem
September 20, 2018 / Jessica Davis
AI and machine learning deployments are hitting the mainstream in enterprises, but executives still hesitate to blindly accept insights from inside the "black box" without seeing the logic behind them. Is your algorithm fair and unbiased? How can you be sure that the insights it offers are correct? It's a question that's being asked with increasing frequency in the last year. That's because when it comes to machine learning, data goes into a "black box" and insights emerge on the other side. The algorithm itself is inside this so-called black box. No one can see inside the box. No one knows why the algorithm arrived at one conclusion or another.