Data Science is increasingly driving many of the choices available to us. Algorithms help online companies recommend content, products and advertisements. Retailers are using them to target people for offers. They narrow down our romantic options and filter and recommend job candidates for corporations. Data algorithms are expected to be more objective than humans, but unfortunately can have bias and even discrimination built into them. Sometimes these outcomes reflect the unconscious biases of engineers; other times such errors are baked into machine learning itself. This panel will discuss the examples of algorithms going wrong and the causes for bias in data science and machine learning. Panelists will explore solutions to this problem, including the need for diversity in the promising field of data science and statistics, as well as approaches companies can implement to weed it out.
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Looks like a great event!
