Mitigating bias in automated decision-making systems, specifically deep learning models, is a critical challenge in achieving fairness. This complexity stems from factors such as nuanced definitions of fairness, unique biases in each dataset, and the trade-off between fairness and model accuracy. To address such issues, we introduce FairVIC, an innovative approach designed to enhance fairness in neural networks by addressing inherent biases at the training stage. FairVIC differs from traditional approaches that typically address biases at the data preprocessing stage. Instead, it integrates variance, invariance and covariance into the loss function to minimise the model's dependency on protected characteristics for making predictions, thus promoting fairness. Our experimentation and evaluation consists of training neural networks on three datasets known for their biases, comparing our results to state-of-the-art algorithms, evaluating on different sizes of model architectures, and carrying out sensitivity analysis to examine the fairness-accuracy trade-off. Through our implementation of FairVIC, we observed a significant improvement in fairness across all metrics tested, without compromising the model's accuracy to a detrimental extent. Our findings suggest that FairVIC presents a straightforward, out-of-the-box solution for the development of fairer deep learning models, thereby offering a generalisable solution applicable across many tasks and datasets.

翻译：缓解自动化决策系统（尤其是深度学习模型）中的偏见是实现公平性的关键挑战。这一复杂性源于多重因素，包括公平性定义的细微差异、每个数据集的独特偏见，以及公平性与模型准确率之间的权衡。为解决此类问题，我们提出FairVIC方法——一种通过在训练阶段处理固有偏见来增强神经网络公平性的创新方案。与通常在数据预处理阶段处理偏见的传统方法不同，FairVIC将方差、不变性和协方差整合到损失函数中，以最小化模型对受保护特征进行预测的依赖性，从而促进公平性。我们的实验与评估包括：在三个已知存在偏见的数据集上训练神经网络、与前沿算法进行结果对比、评估不同规模模型架构的性能，并通过敏感性分析检验公平性-准确率权衡。通过FairVIC实施，我们发现所有测试指标下的公平性均显著提升，且未对模型准确率造成实质性损害。研究结果表明，FairVIC为开发更公平的深度学习模型提供了一种简单直接的即用型解决方案，从而为跨任务与跨数据集的通用性应用提供支持。