![[article image]](https://cacm.acm.org/system/assets/0002/7183/042717_thestackCom_cluster-832x333.large.jpeg?1493310612&1493310612 "Guaranteeing fairness in algorithmic resource allocation.")
Researchers at the University of Maryland have developed a new method to counter algorithmic bias, with the goal of proving algorithms can guarantee fairness in resource allocation.
The researchers applied a two-stage dependent rounding technique, which takes the form of "slowing down" and "early stopping" of algorithmic calculations.
They note the general problem in algorithmic sorting of variables is that outliers tend to get left out of cluster sorting. The team says the further from the center, the less likely that a specific data point will be included in the cluster that is analyzed, reinforcing a bias against statistical variants.
To solve this problem, the researchers created a new Symmetric Randomized Dependent Rounding technique, which modifies existing algorithms to update variables symmetrically, with additional randomization. The team say this ensures markers far from the center are included in the ongoing algorithmic correlation.
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