How to: Avoid Gender Data Bias at Each Stage of the Data Lifecycle

Stage 2, Data Collection


From Scratch


Alternative Collection Methods

Pitfalls and Risks

  • Classification algorithms must be trained on existing datasets.
  • Bias can arise in dataset as well as specific data selection.
  • Only a binary gender variable can be imputed.
  • Predictive models are not 100% accurate.
  • Modeling gender requires deep understanding of data source and bias considerations.
  • There is a steep initial fixed costs associated with implementing data analytics.




Consultant / Data & Analytics

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Abby McCulloch

Abby McCulloch

Consultant / Data & Analytics

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