Can We Balance Accuracy and Fairness in Machine Learning?

Photo by Piret Ilver on Unsplash

rounded up several eye-opening posts that explain how federated learning can mitigate privacy and safety concerns when collecting massive amounts of data; on the TDS Podcast,

chatted with Andy Jones about the implications of scale on AI—how the aforementioned massive datasets open up opportunities we couldn’t have imagined just a few years ago, but also raise new risks.

looks at a practical application of this conundrum when she explains the visual representation of bias and variance in bulls-eye diagrams. Taking a few steps back,

and Dirk Hovy’s article identifies the most pressing issues the authors and their colleagues face in the field of natural learning processing (NLP): “the speed with which models are published and then used in applications can exceed the discovery of their risks and limitations. And as their size grows, it becomes harder to reproduce these models to discover those aspects.”

Original post: https://towardsdatascience.com/can-we-balance-accuracy-and-fairness-in-machine-learning-e44ebd9f800e

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