  Along with the progress of AI democratization, neural networks are being
deployed more frequently in edge devices for a wide range of applications.
Fairness concerns gradually emerge in many applications, such as face
recognition and mobile medical. One fundamental question arises: what will be
the fairest neural architecture for edge devices? By examining the existing
neural networks, we observe that larger networks typically are fairer. But,
edge devices call for smaller neural architectures to meet hardware
specifications. To address this challenge, this work proposes a novel Fairness-
and Hardware-aware Neural architecture search framework, namely FaHaNa. Coupled
with a model freezing approach, FaHaNa can efficiently search for neural
networks with balanced fairness and accuracy, while guaranteed to meet hardware
specifications. Results show that FaHaNa can identify a series of neural
networks with higher fairness and accuracy on a dermatology dataset. Target
edge devices, FaHaNa finds a neural architecture with slightly higher accuracy,
5.28x smaller size, 15.14% higher fairness score, compared with MobileNetV2;
meanwhile, on Raspberry PI and Odroid XU-4, it achieves 5.75x and 5.79x
speedup.
