FairQuantize: Achieving Fairness Through Weight Quantization for Dermatological Disease Diagnosis

Recent studies have demonstrated that deep learning (DL) models for medical image classification may exhibit biases toward certain demographic attributes such as race, gender, and age. Existing bias mitigation strategies often require sensitive attributes for inference, which may not always be available, or achieve moderate fairness enhancement at the cost of significant accuracy decline. To overcome these obstacles, we propose FairQuantize, a novel approach that ensures fairness by quantizing model weights. We reveal that quantization can be used not as a tool for model compression but as a means to improve model fairness. It is based on the observation that different weights in a model impact performance on various demographic groups differently. FairQuantize selectively quantizes certain weights to enhance fairness while only marginally impacting accuracy. In addition, resulting quantized models can work without sensitive attributes as input. Experimental results on two skin disease datasets demonstrate that FairQuantize can significantly enhance fairness among sensitive attributes while minimizing the impact on overall performance.