In order to monitor and prevent bias in AI systems we can use a wide range of
(statistical) fairness measures. However, it is mathematically impossible to
optimize for all of these measures at the same time. In addition, optimizing a
fairness measure often greatly reduces the accuracy of the system (Kozodoi et
al, 2022). As a result, we need a substantive theory that informs us how to
make these decisions and for what reasons. I show that by using Rawls' notion
of justice as fairness, we can create a basis for navigating fairness measures
and the accuracy trade-off. In particular, this leads to a principled choice
focusing on both the most vulnerable groups and the type of fairness measure
that has the biggest impact on that group. This also helps to close part of the
gap between philosophical accounts of distributive justice and the fairness
literature that has been observed (Kuppler et al, 2021) and to operationalise
the value of fairness.

通过使用 Rawls 公正作为选择公平措施及取得平衡的基础，我们可以为 AI 系统中的公平 / 准确度取得权衡提供一个有原则的选择，从而集中关注最脆弱的群体和最有影响该群体的公平措施。

公平度量与权衡导航

Navigating Fairness Measures and Trade-Offs

In this work, we present a novel scheduling framework enabling anytime
perception for deep neural network (DNN) based 3D object detection pipelines.
We focus on computationally expensive region proposal network (RPN) and
per-category multi-head detector components, which are common in 3D object
detection pipelines, and make them deadline-aware. We propose a scheduling
algorithm, which intelligently selects the subset of the components to make
effective time and accuracy trade-off on the fly. We minimize accuracy loss of
skipping some of the neural network sub-components by projecting previously
detected objects onto the current scene through estimations. We apply our
approach to a state-of-art 3D object detection network, PointPillars, and
evaluate its performance on Jetson Xavier AGX using nuScenes dataset. Compared
to the baselines, our approach significantly improve the network's accuracy
under various deadline constraints.

本文提出了一种新颖的调度框架，能够让基于深度神经网络（DNN）的 3D 物体检测管道具有实时感知能力。我们关注计算密集型的区域提案网络（RPN）和异类多头探测器组件，该框架能够使它们具备可感知截止期限的能力。我们提出了一种调度算法，可以智能地选择子组件的子集，从而在运行期间实现时间和精度之间的有效权衡。同时，本文通过估计，将先前检测到的物体投射到当前场景中，以最小化跳过一些神经网络子组件而损失精度。此外，我们将该方法应用于一个最先进的 3D 物体检测网络 PointPillars，并使用根据 nuScenes 数据集评估其在 Jetson Xavier AGX 上的性能。与基线相比，本文的方法显着提高了网络在各种截止期限下的精度。