Most proposed algorithmic fairness techniques require access to demographic attribute data. To meet this requirement, there are a growing number of calls for increased collection of demographic data. Through conversations with Partners, we found that the lack of clarity around the acceptable uses for demographic data poses a key barrier to addressing algorithmic bias in practice.
The Demographic Data Workstream seeks to understand what types of data collection practices and governance frameworks are required to ensure that fairness assessments of algorithmic systems are conducted in the public interest.
Knowing the Risks: A Necessary Step to Using Demographic Data for Algorithmic Fairness
What We Can’t Measure, We Can’t Understand”: Challenges to Demographic Data Procurement in the Pursuit…
As calls for unbiased algorithmic systems increase, so too does the number of individuals working on algorithmic fairness. However, these practitioners often do not have access to the data they need to detect bias.