The integration of machine learning models in various real-world applications is becoming more prevalent to assist humans in their daily decision-making tasks as a result of recent advancements in this field. However, it has been discovered that there is a tradeoff between the accuracy and fairness of these decision-making tasks. In some cases, these AI systems can be unfair by exhibiting bias or discrimination against certain social groups, which can have severe consequences in real life. Inspired by one of the most well-known human learning skills called grouping, we address this issue by proposing a novel machine learning framework where the ML model learns to group a diverse set of problems into distinct subgroups to solve each subgroup using its specific sub-model. Our proposed framework involves three stages of learning, which are formulated as a three-level optimization problem: (i) learning to group problems into different subgroups; (ii) learning group-specific sub-models for problem-solving; and (iii) updating group assignments of training examples by minimizing the validation loss. These three learning stages are performed end-to-end in a joint manner using gradient descent. To improve fairness and accuracy, we develop an efficient optimization algorithm to solve this three-level optimization problem. To further reduce the risk of overfitting in small datasets, we incorporate domain adaptation techniques in the second stage of training. We further apply our method to neural architecture search. Extensive experiments on various datasets demonstrate our method's effectiveness and performance improvements in both fairness and accuracy. Our proposed Learning by Grouping can reduce overfitting and achieve state-of-the-art performances with fixed human-designed network architectures and searchable network architectures on various datasets.

翻译：机器学习模型在各类实际应用中的集成日益普及，以协助人类完成日常决策任务，这得益于该领域的最新进展。然而，研究发现这些决策任务在准确性与公平性之间存在权衡。在某些情况下，这些AI系统可能因对特定社会群体表现出偏见或歧视而存在不公平性，从而在现实生活中产生严重后果。受人类最著名的学习技能之一“分组学习”的启发，我们提出了一种新型机器学习框架来解决该问题：在该框架中，ML模型学会将多样化的任务集合划分为不同子组，并使用特定子模型解决每个子组。我们提出的框架包含三个阶段的学习过程，这些过程被形式化为一个三层优化问题：（i）学习将问题划分为不同子组；（ii）学习用于问题求解的组特定子模型；（iii）通过最小化验证损失来更新训练示例的组分配。这三个学习阶段通过梯度下降以端到端方式联合执行。为提升公平性与准确率，我们开发了一种高效优化算法来解决该三层优化问题。为进一步降低小数据集上的过拟合风险，我们在第二阶段训练中融入了领域自适应技术。我们进一步将所提方法应用于神经架构搜索。在多种数据集上的大量实验证明了我们的方法在公平性与准确率方面的有效性和性能提升。我们提出的“通过分组学习”能够减少过拟合，并在固定人工设计网络架构与可搜索网络架构下，在多种数据集上达到最先进性能。