Sex, Gender and/or Intersectional Analysis in Industrial Products

Product Design:

Consider Sex, Gender and Intersectionality:


Many Companies seek to ensure fairness in their tools. This may require:

  • • Fairness-aware data collection and curation
  • • Overcoming teams’ blind spots
  • • Implementing proactive fairness auditing processes
  • • Deciding how to address particular instances of unfairness
  • • Addressing biases in the humans embedded throughout the ML development pipeline


Works Cited:

Holstein, K., Wortman Vaughan, J., Daumé III, H., Dudik, M., & Wallach, H. (2019, May). Improving fairness in machine learning systems: What do industry practitioners need? In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-16).



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