Modeling and deployment bias

Modeling and deployment bias comes from the model you build, and the way you intend to use it.

The bias sources in modeling, evaluation, and deployment vary.

Articles on modeling and deployment bias

We’ve written more in-depth articles on each type of bias including tips on how to prevent each type:

How to prevent bias in modeling and deployment

Regardless of the different bias types that might occur during the model building process, there are some actions that we can take to prevent bias.

  • Start with being aware of common bias types.

  • Make sure that you have the domain knowledge in that area, or be sure that someone with good domain knowledge audit your model outcomes.

  • Audit your model outcomes on a regular basis to detect changes in use cases over time.

  • Make sure that you have an understanding of the characteristics of different models.

  • Make sure that you have an understanding of the integration of your model in existing workflows.

  • Make sure that you have an understanding of the model and human interaction.

  • Make sure that you understand what type of consequences it will have when your model predictions have high error.

  • Make sure that you can justify the choice of your model and parameters.

Last but not least, make sure that your model is not a decision system but can be used to support a decision system.

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