标题： 一种平滑插值到最优传输的算法以恢复有偏数据实现算法公平性提案 显示英文标题

标题： A proposal of smooth interpolation to optimal transport for restoring biased data for algorithmic fairness

摘要： 所谓算法偏见是在基于人工智能的决策过程中一个热门话题，特别是在涉及人口统计特征（如性别、年龄或种族起源）时。 通常，问题不仅在于算法本身，还在于喂养算法的有偏数据，而这只是社会偏见的反映。 因此，必须修复提供给算法的“食物”，以使其对所有人产生无偏的结果。 作为一个简单的但常见的案例，将考虑两个不同的子组，即特权组和非特权组。 假设结果不应依赖于划分数据的这种特性，那么每个组的其余属性必须被移动（运输），以便可以认为其潜在分布是相似的。 为此，使用最优传输（OT）理论有效地运输特征值（敏感变量除外）到两个分布中每组条件下的所谓{\it 沃瑟斯坦重心}。 为了实现这一点，采用了一种基于{\it 拍卖算法}的高效程序。 该运输是对现有数据进行的。 如果新的数据到达，则必须为新集合解决OT问题，该集合包括之前的数据和即将到达的数据，这效率较低。 相反，本文提出了一种称为\textit{扩展总修复（ExTR）}的平滑插值过程的实现，这是本文的主要贡献之一。 该方法论被成功应用于模拟的有偏数据以及用于风险评估预测的德国信用数据集的真实案例中。 显示英文摘要

摘要： The so-called algorithmic bias is a hot topic in the decision making process based on Artificial Intelligence, especially when demographics, such as gender, age or ethnic origin, come into play. Frequently, the problem is not only in the algorithm itself, but also in the biased data feeding the algorithm, which is just the reflection of the societal bias. Thus, this `food' given to the algorithm have to be repaired in order to produce unbiased results for all. As a simple, but frequent case, two different subgroups will be considered, the privileged and the unprivileged groups. Assuming that results should not depend on such characteristic dividing the data, the rest of attributes in each group have to be moved (transported) so that its underlying distribution can be considered similar. For doing this, optimal transport (OT) theory is used to effectively transport the values of the features, excluding the sensitive variable, to the so-called {\it Wasserstein barycenter} of the two distributions conditional to each group. An efficient procedure based on the {\it auction algorithm} is adapted for doing so. The transportation is made for the data at hand. If new data arrive then the OT problem has to be solved for the new set gathering previous and incoming data, which is rather inefficient. Alternatively, an implementation of a smooth interpolation procedure called \textit{Extended Total Repair (ExTR)} is proposed, which is one of the main contributions of the paper. The methodology is applied with satisfactory results to simulated biased data as well as to a real case for a German credit dataset used for risk assessment prediction.