# Data-Algorithm-Architecture Co-Optimization for Fair Neural Networks on Skin Lesion Dataset

arXiv: https://arxiv.org/abs/2407.13896 · HTML: https://mlsanity.com/paper/2407.13896/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 34 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Adversarial Training for Free!](https://mlsanity.com/paper/1904.12843/) (2019)
- [Learning De-biased Representations with Biased Representations](https://mlsanity.com/paper/1910.02806/) (2019)
- [Resnet in Resnet: Generalizing Residual Architectures](https://mlsanity.com/paper/1603.08029/) (2016)
- [Towards Fair Federated Learning with Zero-Shot Data Augmentation](https://mlsanity.com/paper/2104.13417/) (2021)
- [Hardware/Software Co-Exploration of Neural Architectures](https://mlsanity.com/paper/1907.04650/) (2019)
- [Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate](https://mlsanity.com/paper/2402.02769/) (2024)
- [AutoReP: Automatic ReLU Replacement for Fast Private Network Inference](https://mlsanity.com/paper/2308.10134/) (2023)
- [LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference](https://mlsanity.com/paper/2309.14331/) (2023)
- [Estimating and Improving Fairness with Adversarial Learning](https://mlsanity.com/paper/2103.04243/) (2021)
- [MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training](https://mlsanity.com/paper/2312.08656/) (2023)
- [The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devices](https://mlsanity.com/paper/2202.11317/) (2022)
- [Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation](https://mlsanity.com/paper/2107.04886/) (2021)
- [Toward Fairness Through Fair Multi-Exit Framework for Dermatological Disease Diagnosis](https://mlsanity.com/paper/2306.14518/) (2023)
- [Towards A Reliable Ground-Truth For Biased Language Detection](https://mlsanity.com/paper/2112.07421/) (2021)
- [ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation](https://mlsanity.com/paper/2007.09455/) (2020)
- [Generative Adversarial Networks for Mitigating Biases in Machine Learning Systems](https://mlsanity.com/paper/1905.09972/) (2019)

## Built on by (most cited first, 2 of 2)

- [Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based Image Synthesis](https://mlsanity.com/paper/2507.17860/) (2025)
- [FairCompressAgent: An Agentic Framework for Fairness-Aware Model Compression for FPGA Deployment](https://mlsanity.com/paper/2609.17786/) (2026)

## Abstract (arXiv)

As Artificial Intelligence (AI) increasingly integrates into our daily lives, fairness has emerged as a critical concern, particularly in medical AI, where datasets often reflect inherent biases due to social factors like the underrepresentation of marginalized communities and socioeconomic barriers to data collection. Traditional approaches to mitigating these biases have focused on data augmentation and the development of fairness-aware training algorithms. However, this paper argues that the architecture of neural networks, a core component of Machine Learning (ML), plays a crucial role in ensuring fairness. We demonstrate that addressing fairness effectively requires a holistic approach that simultaneously considers data, algorithms, and architecture. Utilizing Automated ML (AutoML) technology, specifically Neural Architecture Search (NAS), we introduce a novel framework, BiaslessNAS, designed to achieve fair outcomes in analyzing skin lesion datasets. BiaslessNAS incorporates fairness considerations at every stage of the NAS process, leading to the identification of neural networks that are not only more accurate but also significantly fairer. Our experiments show that BiaslessNAS achieves a 2.55% increase in accuracy and a 65.50% improvement in fairness compared to traditional NAS methods, underscoring the importance of integrating fairness into neural network architecture for better outcomes in medical AI applications.