社会机器人技术的发展和欧洲前景实践的发展，将这些基于AI的系统纳入机构医疗保健环境中，要求（重新）根据人类价值和权利来配置我们的实践生活。尽管人们对社会机器人技术的道德含义越来越关注，但目前对其中心分支之一的辩论社会辅助机器人技术（SAR）仍取决于一种贫穷的道德方法。本文介绍并研究了这种现行方法的一些趋势，这些趋势已被批判文献综述所确定。基于对道德反思如何导致社会机器人技术的代表性案例的分析，概述了一些未来的研究行，这可能有助于重塑和加深其道德意义。

2019年，英国的移民和庇护室的上部法庭驳回了基于其他差异的生物识别系统产出的决定。在生物识别数据库中发现了庇护所寻求者的指纹，这与上诉人的账户相矛盾。法庭发现这一证据明确透明，否认庇护索赔。如今，生物识别系统的扩散正在围绕其政治，社会和道德意义塑造公众辩论。然而，虽然对移动控制的种族式使用这种技术的担忧一直在上升，但对生物识别行业的投资和创新正在增加大幅增加。此外，生物识别技术最近也已经采用了公平，以减轻生物识别学的偏见和歧视。然而，算法公平不能在破损或预期目的的情况下分配正义，这是为了区分，例如在边境部署的生物识别。在本文中，我们提供了最近关于生物识别公平性辩论的批判性阅读，并展示了其在机器学习和关键边界研究的公平研究中的局限性。在以前的公平演示中，我们证明了生物识别公平标准是数学上的互斥。然后，纸张继续验证说明公平的生物识别系统，通过从先前的作品中再现实验。最后，我们通过在边境的辩论中讨论生物识别性的公平性的政治。我们声称偏见和错误率对公民和寻求庇护者产生了不同的影响。公平已经在生物识别学室内黯然失色，专注于算法的人口偏见和伦理话语，而不是检查这些系统如何重现历史和政治不公正。

机器人操纵的基准是机器人研究中的开放问题之一。在过去十年中，在该领域实现了进展的一个重要因素是在不同研究组中共享的共同对象集的存在。然而，当涉及具有独特特殊性和挑战的布料物体时，现有的对象集非常有限。本文是朝向从机器人布操纵界的研究组中分发的布对象设置的第一步。我们展示了一组家庭布料对象和相关任务，有助于暴露与收集这种物体集合的挑战，并提出了一种向布操控任务中的共同基准设计的路线图，有意将理由设置为未来的辩论在社区中，有必要促进用于操纵布料物体的基准。还将一些RGB-D和对象扫描作为相关配置中的对象的示例收集。有关布料集的更多细节在http://www.iri.upc.edu/groups/perception/clothobjectset/houble uholdclothset.html中共享。

As machine learning being used increasingly in making high-stakes decisions, an arising challenge is to avoid unfair AI systems that lead to discriminatory decisions for protected population. A direct approach for obtaining a fair predictive model is to train the model through optimizing its prediction performance subject to fairness constraints, which achieves Pareto efficiency when trading off performance against fairness. Among various fairness metrics, the ones based on the area under the ROC curve (AUC) are emerging recently because they are threshold-agnostic and effective for unbalanced data. In this work, we formulate the training problem of a fairness-aware machine learning model as an AUC optimization problem subject to a class of AUC-based fairness constraints. This problem can be reformulated as a min-max optimization problem with min-max constraints, which we solve by stochastic first-order methods based on a new Bregman divergence designed for the special structure of the problem. We numerically demonstrate the effectiveness of our approach on real-world data under different fairness metrics.

We consider the problem of two active particles in 2D complex flows with the multi-objective goals of minimizing both the dispersion rate and the energy consumption of the pair. We approach the problem by means of Multi Objective Reinforcement Learning (MORL), combining scalarization techniques together with a Q-learning algorithm, for Lagrangian drifters that have variable swimming velocity. We show that MORL is able to find a set of trade-off solutions forming an optimal Pareto frontier. As a benchmark, we show that a set of heuristic strategies are dominated by the MORL solutions. We consider the situation in which the agents cannot update their control variables continuously, but only after a discrete (decision) time, $\tau$. We show that there is a range of decision times, in between the Lyapunov time and the continuous updating limit, where Reinforcement Learning finds strategies that significantly improve over heuristics. In particular, we discuss how large decision times require enhanced knowledge of the flow, whereas for smaller $\tau$ all a priori heuristic strategies become Pareto optimal.

The automated machine learning (AutoML) field has become increasingly relevant in recent years. These algorithms can develop models without the need for expert knowledge, facilitating the application of machine learning techniques in the industry. Neural Architecture Search (NAS) exploits deep learning techniques to autonomously produce neural network architectures whose results rival the state-of-the-art models hand-crafted by AI experts. However, this approach requires significant computational resources and hardware investments, making it less appealing for real-usage applications. This article presents the third version of Pareto-Optimal Progressive Neural Architecture Search (POPNASv3), a new sequential model-based optimization NAS algorithm targeting different hardware environments and multiple classification tasks. Our method is able to find competitive architectures within large search spaces, while keeping a flexible structure and data processing pipeline to adapt to different tasks. The algorithm employs Pareto optimality to reduce the number of architectures sampled during the search, drastically improving the time efficiency without loss in accuracy. The experiments performed on images and time series classification datasets provide evidence that POPNASv3 can explore a large set of assorted operators and converge to optimal architectures suited for the type of data provided under different scenarios.

The quality of consequences in a decision making problem under (severe) uncertainty must often be compared among different targets (goals, objectives) simultaneously. In addition, the evaluations of a consequence's performance under the various targets often differ in their scale of measurement, classically being either purely ordinal or perfectly cardinal. In this paper, we transfer recent developments from abstract decision theory with incomplete preferential and probabilistic information to this multi-target setting and show how -- by exploiting the (potentially) partial cardinal and partial probabilistic information -- more informative orders for comparing decisions can be given than the Pareto order. We discuss some interesting properties of the proposed orders between decision options and show how they can be concretely computed by linear optimization. We conclude the paper by demonstrating our framework in an artificial (but quite real-world) example in the context of comparing algorithms under different performance measures.

Methods for learning optimal policies use causal machine learning models to create human-interpretable rules for making choices around the allocation of different policy interventions. However, in realistic policy-making contexts, decision-makers often care about trade-offs between outcomes, not just singlemindedly maximising utility for one outcome. This paper proposes an approach termed Multi-Objective Policy Learning (MOPoL) which combines optimal decision trees for policy learning with a multi-objective Bayesian optimisation approach to explore the trade-off between multiple outcomes. It does this by building a Pareto frontier of non-dominated models for different hyperparameter settings. The key here is that a low-cost surrogate function can be an accurate proxy for the very computationally costly optimal tree in terms of expected regret. This surrogate can be fit many times with different hyperparameter values to proxy the performance of the optimal model. The method is applied to a real-world case-study of conditional cash transfers in Morocco where hybrid (partially optimal, partially greedy) policy trees provide good performance as a surrogate for optimal trees while being computationally cheap enough to feasibly fit a Pareto frontier.

Modern machine learning models are often constructed taking into account multiple objectives, e.g., to minimize inference time while also maximizing accuracy. Multi-objective hyperparameter optimization (MHPO) algorithms return such candidate models and the approximation of the Pareto front is used to assess their performance. However, when estimating generalization performance of an approximation of a Pareto front found on a validation set by computing the performance of the individual models on the test set, models might no longer be Pareto-optimal. This makes it unclear how to measure performance. To resolve this, we provide a novel evaluation protocol that allows measuring the generalization performance of MHPO methods and to study its capabilities for comparing two optimization experiments.

In this paper, we show the surprisingly good properties of plain vision transformers for body pose estimation from various aspects, namely simplicity in model structure, scalability in model size, flexibility in training paradigm, and transferability of knowledge between models, through a simple baseline model dubbed ViTPose. Specifically, ViTPose employs the plain and non-hierarchical vision transformer as an encoder to encode features and a lightweight decoder to decode body keypoints in either a top-down or a bottom-up manner. It can be scaled up from about 20M to 1B parameters by taking advantage of the scalable model capacity and high parallelism of the vision transformer, setting a new Pareto front for throughput and performance. Besides, ViTPose is very flexible regarding the attention type, input resolution, and pre-training and fine-tuning strategy. Based on the flexibility, a novel ViTPose+ model is proposed to deal with heterogeneous body keypoint categories in different types of body pose estimation tasks via knowledge factorization, i.e., adopting task-agnostic and task-specific feed-forward networks in the transformer. We also empirically demonstrate that the knowledge of large ViTPose models can be easily transferred to small ones via a simple knowledge token. Experimental results show that our ViTPose model outperforms representative methods on the challenging MS COCO Human Keypoint Detection benchmark at both top-down and bottom-up settings. Furthermore, our ViTPose+ model achieves state-of-the-art performance simultaneously on a series of body pose estimation tasks, including MS COCO, AI Challenger, OCHuman, MPII for human keypoint detection, COCO-Wholebody for whole-body keypoint detection, as well as AP-10K and APT-36K for animal keypoint detection, without sacrificing inference speed.