Washington D.C. – In its most basic form, artificial intelligence is an algorithm that is trained to learn via the data that is fed to it. But what happens when that data is full of bias? "In traditional model building, even with good data we can introduce biases by not constructing the right variables or picking up nuances. A model is a representative of the mechanism that generated the data. So if we don't represent that mechanism correctly, then we are not forecasting correctly, but forecasting something else," explains Oliver Schabenberger, chief technology officer and executive vice president of SAS Institute Inc. to Canadian media at the Analytics Experience 2017.

Washington D.C. – In its most basic form, artificial intelligence is an algorithm that is trained to learn via the data that is fed to it. But what happens what that data is full of bias? "In traditional model building, even with good data we can introduce biases by not constructing the right variables or picking up nuances. A model is a representative of the mechanism that generated the data. So if we don't represent that mechanism correctly, then we are not forecasting correctly, but forecasting something else," explains Oliver Schabenberger, chief technology officer and executive vice president of SAS Institute Inc. to Canadian media at the Analytics Experience 2017.