Human insight remains essential to beat the bias of algorithms

Better data can improve AI’s ability to spot correlations but will not ensure fairness.

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.

Without the insight of human problem solvers driving our questions, “better numbers” mean nothing and our algorithms will never do more than reflect our own biases.

 

 

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