This paper critically examines existing modes of participation in design
practice and machine learning. Cautioning against 'participation-washing', it
suggests that the ML community must become attuned to possibly exploitative and
extractive forms of community involvement and shift away from the prerogatives
of context-independent scalability.

论文批判性地审视了设计实践和机器学习中现有的参与模式，提醒需要注意可能存在的剥削性和提取性的社区参与形式，并转向考虑上下文无关可扩展性的优先权。

参与并非机器学习的设计修复

Participation is not a Design Fix for Machine Learning

Structural optimization is a popular method for designing objects such as
bridge trusses, airplane wings, and optical devices. Unfortunately, the quality
of solutions depends heavily on how the problem is parameterized. In this
paper, we propose using the implicit bias over functions induced by neural
networks to improve the parameterization of structural optimization. Rather
than directly optimizing densities on a grid, we instead optimize the
parameters of a neural network which outputs those densities. This
reparameterization leads to different and often better solutions. On a
selection of 116 structural optimization tasks, our approach produces the best
design 50% more often than the best baseline method.

本文提出，使用神经网络所引入的函数隐式偏置以改善结构优化的参数化，通过优化神经网络参数来代替在网格上直接优化密度，使得优化结果更为理想，在 116 个结构优化任务中，我们的方法比最佳基线方法更为有效。

神经重新参数化改善结构优化

Neural reparameterization improves structural optimization

As technology become more advanced, those who design, use and are otherwise
affected by it want to know that it will perform correctly, and understand why
it does what it does, and how to use it appropriately. In essence they want to
be able to trust the systems that are being designed. In this survey we present
assurances that are the method by which users can understand how to trust this
technology. Trust between humans and autonomy is reviewed, and the implications
for the design of assurances are highlighted. A survey of research that has
been performed with respect to assurances is presented, and several key ideas
are extracted in order to refine the definition of assurances. Several
directions for future research are identified and discussed.

本文调查了技术设计及使用中存在的信任问题，提出了保证技术可信的方法，并总结出几个关键思路，为以后的保证设计提供了指导。

算法保证在人 - 自主权信任关系中的定义、案例和调查

"I can assure you [$\ldots$] that it's going to be all right" -- A  definition, case for, and survey of algorithmic assurances in human-autonomy  trust relationships