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.

本文介绍了 AMSAL 算法和它在多个数据集上的测试，其中包括一个 Twitter 数据集和带有各种类型的保护属性的 BiasBench 基准测试。该算法旨在在信息需要被删除，但不是直接与输入示例对齐的情况下，从神经表示中删除信息。

神经表示中未对齐属性的擦除

Erasure of Unaligned Attributes from Neural Representations

When allocating indivisible items to agents, it is known that the only
strategyproof mechanisms that satisfy a set of rather mild conditions are
constrained serial dictatorships: given a fixed order over agents, at each step
the designated agent chooses a given number of items (depending on her position
in the sequence). With these rules, also known as non-interleaving picking
sequences, agents who come earlier in the sequence have a larger choice of
items. However, this advantage can be compensated by a higher number of items
received by those who come later. How to balance priority in the sequence and
number of items received is a nontrivial question. We use a previous model,
parameterized by a mapping from ranks to scores, a social welfare functional,
and a distribution over preference profiles. For several meaningful choices of
parameters, we show that the optimal sequence can be computed in polynomial
time. Last, we give a simple procedure for eliciting scoring vectors and we
study the impact of the assignment from agents to positions on the ex-post
social welfare.

研究机制，序列式独裁，分配，社会福利和代理人影响的影响

关于策略无懈可击和公正选取序列的研究：第一位玩家不可全选

Thou Shalt not Pick all Items if Thou are First: of Strategyproof and Fair Picking Sequences

Strategic behavior is a fundamental problem in a variety of real-world
applications that require some form of peer assessment, such as peer grading of
homeworks, grant proposal review, conference peer review of scientific papers,
and peer assessment of employees in organizations. Since an individual's own
work is in competition with the submissions they are evaluating, they may
provide dishonest evaluations to increase the relative standing of their own
submission. This issue is typically addressed by partitioning the individuals
and assigning them to evaluate the work of only those from different subsets.
Although this method ensures strategyproofness, each submission may require a
different type of expertise for effective evaluation. In this paper, we focus
on finding an assignment of evaluators to submissions that maximizes assigned
evaluators' expertise subject to the constraint of strategyproofness. We
analyze the price of strategyproofness: that is, the amount of compromise on
the assigned evaluators' expertise required in order to get strategyproofness.
We establish several polynomial-time algorithms for strategyproof assignment
along with assignment-quality guarantees. Finally, we evaluate the methods on a
dataset from conference peer review.

本文研究评估者分配问题，旨在解决评估者保真行为与专业领域匹配性的矛盾，结合多项式时间算法进行实现，并通过会议同行评审数据集进行方法的评估。