Performance, Opaqueness, Consequences, and Assumptions: Simple questions for responsible planning of machine learning solutions. (arXiv:2208.09966v1 [cs.LG])

The data revolution has generated a huge demand for data-driven solutions.
This demand propels a growing number of easy-to-use tools and training for
aspiring data scientists that enable the rapid building of predictive models.
Today, weapons of math destruction can be easily built and deployed without
detailed planning and validation. This rapidly extends the list of AI failures,
i.e. deployments that lead to financial losses or even violate democratic
values such as equality, freedom and justice. The lack of planning, …

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