Summary contributed by Yi Sheng, a Ph.D. student at George Mason University, advised by Weiwen Jiang, and interested in software and hardware co-design, AutoML, and dermatology diagnosis.
[Original paper by Yi Sheng, Junhuan Yang, Yawen Wu, Kevin Mao, Yiyu shi, Jingtong Hu, Weiwen Jiang, Lei Yang]
Overview: Along with the progress of AI democratization, neural networks are being deployed more frequently in edge devices for a wide range of applications. Therefore, fairness concerns gradually emerge in applications such as face recognition and mobile medical. One fundamental question arises: what will be the fairest neural architecture for edge devices? To address this challenge, a new work proposes a novel framework, FaHaNa.
Many research efforts have been put into addressing the fairness issues in AI applications. However, they either focus on the model interpretability by modifying the neural network models to be fairer, or fairness-aware data collection. While this can mitigate unfairness, there is still a blind spot: neural networks need to be small enough to accommodate limited computation power and memory/storage space for edge devices. A new paper by Yi Sheng and co-authors seeks to solve this problem. By examining the existing networks, they observe that larger neural networks are typically fairer. However, is there a possibility that the model is also very fair while controlling model size? This paper uses a new neural architecture search (NAS) framework search for neural networks with balanced fairness and accuracy while guaranteed to meet the hardware specifications. FaHaNa is presented, and it is proved with high fairness and accuracy on a dermatology dataset.
Motivation and observation
- Existing neural networks, including MobileNet, MnasNet, ProxylessNAS, and ResNet are shown with unfairness scores. These models have a prejudice against light skins which is the majority group, and dark skins, which is the minority group. At the same time, there exists an inherent imbalance since data from minority groups may not be easily collected due to objective reasons. Such a result motivates us to find a more equitable model.
- Fairness, accuracy, and hardware efficiency are equally crucial in edge AI applications like medical AI. Losing any one of these characteristics will render the architecture useless. Existing networks either have accuracy issues (SqueezeNet) or size issues (MobileNet).
- Different groups obtain the variation of intermediate features after each layer in the neural network. Front layers have less impact, but the model’s tail affects the fairness more. Thus, NAS will target more on the tail search and freeze the head.
There are four components in the FaHaNa framework: a recurrent neural network (RNN) based controller, a block-based search space, a backbone architecture producer, a performance evaluator, and a trainer. In particular, the controller will guide the optimization process. From block-based search space, it will identify the searchable block in the backbone architecture to form a neural network (child network). Then, the child network will be sent to the trainer to learn the function. Simultaneously, the evaluator will get the latency on the given hardware. Finally, a reward will be generated to update RNN in the controller.
FaHaNa, geared to run on mobile phones, is evaluated on a dermatology dataset for diagnosing dermatological diseases. Therefore, we apply two edge devices, Raspberry PI and Odroid XU-4, as our testbed. Results show that FaHaNa can improve fairness without compromising accuracy, meanwhile reducing the model size.
FaHaNa-fair is the fairest architecture in the FaHaNa series. FaHaNa-small is the smallest network that is suitable for edge devices.
Between the Lines
The findings of this research present some exciting takeaways. Fairness between light and dark skins is an essential point in AI democratization. FaHaNa integrates fairness in NAS for the first time to design fairer neural architectures. In addition, a freezing method has been proposed to accelerate the NAS process. As such, FaHaNa can identify a series of neural architectures forming a much better Pareto frontier on accuracy, fairness, and model size than existing neural architectures. In terms of the next steps, it would be highly beneficial for researchers to pay more attention to architectural fairness.