Design for Human Error

From The Foundation for Best Practices in Machine Learning

Design for Human Error


(a) Understand the causes of error and design to minimise those causes; (b) Do sensibility checks. Does the action pass the "common sense" test (e.g. is the number is correct? - 10.000g or 10.000kg) (c) Make it possible to reverse actions - to "undo" them - or make it harder to do what cannot be reversed (eg. add constraints to block errors - either change the color to red or mention "Do you want to delete this file? Are you sure?"). (d) make it easier for people to discover the errors that do occur, and make them easier to correct


To (a) increase trust between the end user and the model; (b) minimize the opportunities for errors while also mitigating the consequences. Increase the trust users have with your product by design for deliberate mis-use of your model (making your model or product "idiot-proof") so users are (a) able to insert data to compare the model outcome with their own expected outcome which will increase their trust, or (b) users able to test the limitations of your product or model -via fake or highly unlikely data- without breaking your product or model.

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