Why do ML engineers struggle to build trustworthy ML applications

 By Medium - 2021-03-16

Description

While research on the Ethics of AI increased significantly in recent years, practitioners tend to neglect the risks coming from inequity or unfairness of their ML components. This is one of the…

Summary

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  • As we discuss in more detail below, our overall conclusion is that ML engineering teams lack concrete courses of action for designing and building their systems specifically to satisfy trustworthiness requirements, such as security, robustness, and fairness.
  • Unfortunately, we found out that the practices for trustworthy ML have relatively low adoption (as can be seen in the figure above).
  • The contributing factors to these results are diverse.

 

Topics

  1. Management (0.29)
  2. Machine_Learning (0.2)
  3. Backend (0.17)

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