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.

New robot to help online students learn better - Scientists have developed an innovative robot that can help online students more engaged and connected to the instructor and students in the classroom. Stationed around the class, each robot has a mounted video screen controlled by the remote user that lets the student pan around the room to see and talk with the instructor and fellow students participating in-person....