Scalable Spectral Clustering with Group Fairness Constraints

Fairness in clustering can now be achieved at scale: The new s-FairSC algorithm makes it practical to apply fairness constraints to clustering tasks on larger datasets, speeding up computations by a factor of 12 compared to previous methods, which is important since fairness considerations are often abandoned in practice due to computational burden.

Statistical parity is the fairness goal: The paper focuses on "group fairness" or "statistical parity," meaning each protected group (e.g., different demographic groups) appears in each cluster at the same proportion as in the overall dataset—a concrete, measurable fairness standard that prevents systematic underrepresentation.

Technical efficiency enables responsible AI deployment: By reformulating the underlying mathematics to work with sparse data structures rather than dense matrices, s-FairSC reduces computational costs to only marginally exceed standard clustering without fairness constraints, making fair algorithms more feasible for real-world deployment.

Fair clustering quality is maintained: Experimental results show that the faster s-FairSC algorithm recovers fair clustering results comparable to the slower FairSC method, demonstrating that speed improvements don't come at the cost of fairness quality.

Scalability removes a barrier to fairness in practice: The ability to apply fairness constraints without prohibitive computational costs addresses a key obstacle to implementing responsible AI—organizations can now consider fairness without sacrificing performance on large datasets.