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Saturday, January 13, 2018

How machine learning engineers can detect and debug algorithmic bias | Boing Boing

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"Ben Lorica, O'Reilly's chief data scientist, has posted slides and notes from his talk at last December's Strata Data Conference in Singapore, "We need to build machine learning tools to augment machine learning engineers."" notes Cory Doctorow, Writer, blogger, activist.

Photo: Boing Boing

Lorica describes a new job emerging in IT departments: "machine learning engineers," whose job is to adapt machine learning models for production environments. These new engineers run the risk of embedding algorithmic bias into their systems, which unfairly discriminate, create liability, and reduces the quality of the recommendations the systems produce. 

He presents a set of technical and procedural steps to take to minimize these risks, with links to the relevant papers and code. It's really required reading for anyone implementing a machine learning system in a production environment. 

We need to build machine learning tools to augment machine learning engineers [Ben Lorica/O'Reilly]
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Source: Boing Boing


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