Design Inclusive Machine Learning
Yi-Ying Lin

Hey Yi-Ying nice article on how to think about machine learning from the UX perspective.

Going forward designers will need to understand the biases of the data thats going into the system, those of the users — hardest yet the biases of the algorithms. The last is as important because it will be the designers work to mitigate the users misunderstanding, anthropomorphizing and trust issues caused by the differences between the users model of how (or that) the software “thinks” and their own internal models.

Another part of the overall experience is handling the nature of encouraging users training of the systems. in some situations it will either be easy to get user to correct the system because it is fun, transparent or obviously adds value. Otherwise they will need to be guided to teaching the system to the appropriate or correct goal with guide rails to stop the system from being gamed.

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