We present the Assignment-Maximization Spectral Attribute removaL (AMSAL) algorithm, which aims at removing information from neural representations when the information to be erased is implicit rather than directly being aligned to each input example. Our algorithm works by alternating between two steps. In one, it finds an assignment of the input representations to the information to be erased, and in the other, it creates projections of both the input representations and the information to be erased into a joint latent space. We test our algorithm on an extensive array of datasets, including a Twitter dataset with multiple guarded attributes, the BiasBios dataset and the BiasBench benchmark. The latter benchmark includes four datasets with various types of protected attributes. Our results demonstrate that bias can often be removed in our setup. We also discuss the limitations of our approach when there is a strong entanglement between the main task and the information to be erased.

翻译：我们提出分配-最大化谱属性移除（AMSAL）算法，旨在从神经表示中移除信息，其中待擦除信息是隐式的，而非直接与每个输入样本对齐。我们的算法通过在两个步骤之间交替运行：一步中，算法找到输入表示与待擦除信息之间的分配关系；另一步中，算法将输入表示和待擦除信息投影到联合潜在空间中。我们在大量数据集上测试了该算法，包括包含多个受保护属性的Twitter数据集、BiasBios数据集以及BiasBench基准测试。后者包含四个具有不同类型受保护属性的数据集。结果表明，在我们的设置中偏见通常可以被移除。我们还讨论了当主要任务与待擦除信息存在强关联时，本方法的局限性。