How Does Datapeople Determine Gender? An Algorithm, Why?

Understanding the diversity of your talent pool is crucial for building a fair and inclusive work environment. In the realm of gender representation, traditional methods that rely on self-reported data often fall short, leaving recruiters with incomplete or unreliable data. That's where Datapeople's innovative gender inference model steps in, offering valuable insights without infringing on individual privacy.

Understanding the diversity of your talent pool is crucial for building a fair and inclusive work environment. In the realm of gender representation, traditional methods that rely on self-reported data often fall short, leaving recruiters with incomplete or unreliable data. That’s where Datapeople’s innovative gender inference model steps in, offering valuable insights without infringing on individual privacy.  

How does it work?

We leverage a proprietary algorithm trained on vast public datasets of self-reported gender and first names, encompassing a global range of combinations beyond just US and English names. This model analyzes applicant names, providing probabilistic estimates of gender which have been proven, in our tests and our real-world customer usage, to outperform and be more consistently available as compared to self-reported data. 

Why model gender representation?

While self-reporting remains the ideal source for identity data, its incompleteness within an ATS creates a major hurdle. Our model bridges this gap, providing reasonable predictions for the entire candidate pool, not just those who opt to disclose their gender. This comprehensive view paints a clearer picture of potential biases and allows you to make informed decisions to promote fairness.

To ensure the completeness of information as critical as candidate gender, Datapeople was not satisfied with using self-reported data. We know that self-report data is which is both notoriously sparse and often incomplete. This would lead to major blindspots in assessing gender trends throughout your hiring funnel. We get a more accurate result with our global probability model that looks at first names compared to ATS data, which is generally challenged with incomplete, unavailable, or missing data.

How accurate is the model?

Our probabilistic model delivers over 90% accuracy when compared to actual self-reports. This enhanced accuracy and applicability allow you to consistently track gender performance throughout your pipeline, ensuring that you can identify and appropriately address breakdowns. 

Why do we only highlight female and male genders?

We understand the limitations of binary classifications and the importance of inclusivity. Thus we do not report on individual candidate gender, and our aggregate analyses also do not seek to apply non-binary identities due to the complexities of relying upon name-based predictions. Our focus is on providing reliable overall trends that highlight potential issues within your hiring pipeline.

Why does It only apply In aggregate?

Datapeople delivers aggregate gender representation data, empowering you to see the bigger picture. We don’t identify individual applicants, but instead inform you about the demographic makeup of your talent pool as a whole. By analyzing the data in terms of “trends”, we mitigate the known limitations of the model while smartly deploying the potential of assignment across your pipeline.  However, for your entire pipeline, we are highly confident that the majority of someone’s identification will highly correlate to their first name. 

The datapeople gender inference model allows you to:

Ultimately, Datapeople’s gender inference model is a powerful tool for fostering inclusivity and fairness in your recruitment practices. By providing insightful data without compromising individual privacy, we empower you to make informed decisions and build a diverse workforce. A workforce that reflects the richness of the talent pool around you. By better understanding the nuances of your talent pipeline, you will:

  • Identify potential bottlenecks: Are certain genders underrepresented at specific stages of the hiring process? This can be seen in action in our Gender Report
  • Track hiring outcomes: Is there a larger than anticipated discrepancy between candidate gender in the early stages of your pipeline and those that receive and accept offers? By embracing data, easily visualized in Pass-Through Report you can immediately identify and rectify issues.
  • Measure progress over time: Are your efforts to diversify your workforce bearing fruit? Understand the implications of your efforts and turn ambiguous recruiting data into your data-driven hiring strategy.

Ready to build superior diversity into your talent pool? Do so smartly by unlocking deeper and more consistently available insights about your hiring process. Learn more about our Datapeople Insights offering to turn recruiting unknowns into opportunities. 

Together let’s build a future where everyone has the opportunity to thrive.

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