When it comes to bias and artificial intelligence, there is a common belief that algorithms are only as good as the numbers plugged into them. But the focus on algorithmic bias being concentrated entirely on data has meant we have ignored two aspects of this problem: the deep limitations of existing algorithms and, more importantly, the role of human problem solvers. Powerful as they may be, most of our algorithms only mine correlational relationships without understanding anything about them. My research has found that massive data sets on jobs, education and loans contain more spurious correlations than meaningful causal relationships. It is ludicrous to assume these algorithms will solve problems that we do not understand.

When it comes to bias and artificial intelligence, there is a common belief that algorithms are only as good as the numbers plugged into them. But the focus on algorithmic bias being concentrated entirely on data has meant we have ignored two aspects of this problem: the deep limitations of existing algorithms and, more importantly, the role of human problem solvers. Powerful as they may be, most of our algorithms only mine correlational relationships without understanding anything about them. My research has found that massive data sets on jobs, education and loans contain more spurious correlations than meaningful causal relationships. It is ludicrous to assume these algorithms will solve problems that we do not understand.

On March 15, DeepMind's AlphaGo, a computer powered by a self-learning artificial intelligence computer program, defeated Go grandmaster Lee Sedol. As the AI community celebrates this major milestone in making machines smart, the debate of "man vs. machine" is heating up. Over the past 25 years -- especially the last five years -- the AI community has transformed theoretical machine learning constructs to solve useful problems. AI techniques such as self-learning, reinforcement learning, and deep neural networks were developed to recognize traffic signs and classify images. The recent rapid progress in AI was powered by the dramatic increase in financial investments in AI.