This conference focuses on what will happen as we are all increasingly affected by decisions that are based on data and algorithms. For instance, algorithms are now often used to determine who is invited to an interview for a job. Or they can help decide who is approved for a home loan. But algorithms can be biased, and when they are, the use of algorithms reinforces existing social inequalities.
So how can we use data and algorithms in a way that avoids discrimination and thus supports a democratic society?
PayAnalytics’ years of firsthand experience with customers have given us some insight into these questions. David will talk about how to ensure fairness in algorithmic models. This includes what variables organizations should include (or exclude) and how to measure them in an equitable, consistent way. He will also talk about making sure that the variables are not tainted and that the model appropriately accounts for factors like performance and job level.
Additionally, he will discuss our mission to close the gender pay gap. This includes how we use regression analysis to examine both the adjusted and unadjusted pay gaps.
We hope that sharing our experience will help other organizations prevent machine learning bias, support fairness, and use data for good. To learn more about the conference, please visit this link.
