How to Ethically Use Machine Learning to Drive Decisions


Having good high quality information is a big problem in itself. We advocate corporations that need to leverage machine studying, synthetic intelligence, and information science to think about Monica Rogati’s AI Hierarchy of Needs, which has machine studying near the highest as one of many closing items of the puzzle.

Supply: Hackernoon

This hierarchy illustrates that earlier than machine studying can occur, you want stable information foundations and instruments for extracting, loading, and reworking information (ETL), in addition to instruments for cleansing and aggregating information from disparate sources.

This requires sturdy data engineering practices—you’ll must leverage databases, perceive how you can course of information accurately, schedule your workflows, and make use of cloud computing.

So earlier than you rent your first machine studying engineer, it’s best to first arrange your information engineering, information science, and information evaluation capabilities.

Watch out for bias in your information and algorithms

Machine studying can solely be nearly as good as the info you feed it. In case your information is biased, your mannequin shall be too. For instance, Amazon built a ML recruiting tool to foretell the success of candidates primarily based on resumes with ten years’ price of coaching information that favored males as a consequence of historic male dominance throughout the tech business—which triggered the ML instrument to even be biased towards ladies.

That is why information ethics has emerged as such an necessary subject in recent times. As increasingly information is generated, the impression of how that information is used additionally scales dramatically. This requires principled consideration and monitoring. As Cassie Kozyrkov, Google’s Chief Choice Scientist, has analogized, a trainer is barely nearly as good because the books they’re utilizing to show the scholars. If the books are biased, their classes shall be too.

Maintain tabs in your mannequin and enhance it

Keep in mind that the job of machine studying doesn’t finish when your mannequin is in manufacturing, making predictions, or performing classifications. Fashions which can be deployed and doing work nonetheless have to be monitored and maintained.

You probably have a mannequin predicting bank card fraud primarily based on transaction information, you get helpful data each time your mannequin makes a prediction and also you act on it. On prime of this, the exercise you’re making an attempt to observe and predict—on this case, bank card fraud—could also be dynamic and alter over time. This course of, the place information that’s generated is continually in flux, is named information drift—and it proves how important it’s to often replace your mannequin.

Supply: DataBricks


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