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Proceedings of the 1st International Conference on AI for People: Towards Sustainable AI, CAIP 2021, 20-24 November 2021, Bologna, Italy最新文献

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An Object Detection and Scaling Model for Plastic Waste Sorting 塑料垃圾分类的目标检测和缩放模型
A. Padalkar, Pramod Pathak, Paul Stynes
. Plastic waste sorting involves the separation of plastic into its individual plastic types. This research proposes an Object Detection and Scaling Model for plastic waste sorting to detect four types of plastics using the WaDaBa dataset. This research compares the Object Detection and Scaling Models Scaled-Yolov4 and EfficientDet. Results demonstrate that Scaled-Yolov4-CSP outperforms the state of the art, Colour-Histogram based Canny-Edge-Gaussian Filter, by 21% accuracy.
. 塑料垃圾分类包括将塑料分成不同的塑料类型。本研究提出了一个塑料垃圾分类的目标检测和缩放模型,利用WaDaBa数据集检测四种类型的塑料。本研究比较了scaledyolo4和EfficientDet的目标检测和缩放模型。结果表明,Scaled-Yolov4-CSP比基于颜色直方图的cony - edge - gaussian Filter的准确率高21%。
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引用次数: 3
Towards Functional Safety Compliance of Recurrent Neural Networks 递归神经网络功能安全符合性研究
D. Bacciu, Antonio Carta, Daniele Di Sarli, C. Gallicchio, Vincenzo Lomonaco, Salvatore Petroni
Deploying Autonomous Driving systems requires facing some novel challenges for the Automotive industry. One of the most critical aspects that can severely compromise their deployment is Functional Safety. The ISO 26262 standard provides guidelines to ensure Functional Safety of road vehicles. However, this standard is not suitable to develop Artificial Intelligence based systems such as systems based on Recurrent Neural Networks (RNNs). To address this issue, in this paper we propose a new methodology, composed of three steps. The first step is the robustness evaluation of the RNN against inputs perturbations. Then, a proper set of safety measures must be defined according to the model’s robustness, where less robust models will require stronger mitigation. Finally, the functionality of the entire system must be extensively tested according to Safety Of The Intended Functionality (SOTIF) guidelines, providing quantitative results about the occurrence of unsafe scenarios, and by evaluating appropriate Safety Performance Indicators.
对于汽车行业来说,部署自动驾驶系统需要面临一些新的挑战。可能严重影响其部署的最关键方面之一是功能安全。ISO 26262标准提供了确保道路车辆功能安全的指导方针。然而,该标准并不适合开发基于人工智能的系统,例如基于递归神经网络(rnn)的系统。为了解决这一问题,本文提出了一种新的方法,由三个步骤组成。第一步是RNN对输入扰动的鲁棒性评估。然后,必须根据模型的稳健性定义一套适当的安全措施,而不那么稳健性的模型将需要更强的缓解措施。最后,整个系统的功能必须根据预期功能安全(SOTIF)指南进行广泛的测试,提供有关不安全场景发生的定量结果,并通过评估适当的安全性能指标。
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引用次数: 2
René Laloux’s vision of Ecotopian AI: Exploring the Ecosystemic AI through Fantastic Planet ren<s:1> Laloux对生态乌托邦AI的看法:通过《神奇星球》探索生态系统AI
Amar Singh, Shipra Tholia
Some recent experiments with AI, such as MIT’s psychic AI Norman, Microsoft’s Nazi Tay, Amazon’s 2016 racial fiasco of Prime program subscribers, and many others, have exposed the vulnerability of developing AI solely based on human experiences. Such development shall only serve the anthropogenic causes (that too gendered and racially motivated), neglecting the interests of other species. However, ecosystemic artificial intelligence provides an alternative approach where AI interacts and learns from a broad community of species. Learning as such AI adapts itself, privileging the coherence and unity that an ecosystem demands. René Laloux’s animated film Fantastic Planet (1973) focuses on this ecosystemic interaction of AI. The film highlights the positive changes that can be brought in subdued communities when engaged with AI, leading to engendering harmony. René Laloux’s conception of AI comes with the idea of how it can serve in assimilating the marginalized sections within the mainstream by empowering them. This paper delves into examining the situations that the film brings forth, which becomes vital in understanding our relationship to the earth at present, and our role moving forward into the future.
最近的一些人工智能实验,如麻省理工学院的通灵人工智能诺曼、微软的纳粹Tay、亚马逊2016年Prime会员的种族惨败,以及许多其他实验,都暴露了仅仅根据人类经验开发人工智能的脆弱性。这样的发展只会服务于人为的原因(过于性别化和种族化),而忽视了其他物种的利益。然而,生态系统人工智能提供了另一种方法,人工智能可以从广泛的物种群落中相互作用和学习。人工智能这样的学习可以自我适应,赋予生态系统所需的连贯性和统一性特权。雷诺·拉鲁的动画电影《神奇星球》(1973)关注的是人工智能的生态系统互动。这部电影强调了当与人工智能接触时,可以给压抑的社区带来积极的变化,从而产生和谐。ren Laloux的人工智能概念提出了一个想法,即人工智能如何通过赋予边缘化群体权力来同化主流群体。本文深入研究了电影所带来的情况,这对于理解我们目前与地球的关系以及我们在未来的角色至关重要。
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引用次数: 0
The Ethics of Early Crisis Detection - Big Data, AI, and Algorithms in the German Military 早期危机检测的伦理——德国军队中的大数据、人工智能和算法
Lea Buchhorn
Technological developments have and will continue to influence our everyday lives. One of them, AI, promises many benefits in various fields, such as medicine, agriculture, or the military. On the other hand, AI advancement encompasses multifaceted risks and challenges, such as data privacy concerns, opaque decision-making, or discrimination against groups or individuals. AI and Big Data have gained more and more importance in military operations all over the globe. The German military has been trailing different approaches to AI-based early crisis detection applications. However, the more insights are gained about AI and the harm human errors in designing algorithms can cause, the more ethical concerns arise. Thus, this paper investigates which ethical challenges the German military is facing while testing and trying to implement AI-based early crisis detection systems.
科技的发展已经并将继续影响我们的日常生活。其中之一是人工智能,它有望在医学、农业或军事等各个领域带来许多好处。另一方面,人工智能的发展也包含多方面的风险和挑战,例如数据隐私问题、不透明的决策或对群体或个人的歧视。人工智能和大数据在全球军事行动中的作用越来越重要。德国军方一直在研究基于人工智能的早期危机检测应用的不同方法。然而,对人工智能和设计算法时人为错误可能造成的危害的了解越多,就会出现更多的伦理问题。因此,本文调查了德国军方在测试和尝试实施基于人工智能的早期危机检测系统时面临的道德挑战。
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引用次数: 0
Representational bias in expression and annotation of emotions in audiovisual databases 视听数据库中情绪表达与注释的表征性偏差
William Saakyan, Olya Hakobyan, Hanna Drimalla
Emotion recognition models can be confounded by representation bias, where populations of certain gender, age or ethnoracial characteristics are not sufficiently represented in the training data. This may result in erroneous predictions with consequences of personal relevance in sensitive contexts. We systematically examined 130 emotion (audio, visual and audio-visual) datasets and found that age and ethnoracial background are the most affected dimensions, while gender is largely balanced in emotion datasets. The observed disparities between age and ethnoracial groups are compounded by scarce and inconsistent reports of demographic information. Finally, we observed a lack of information about the annotators of emotion datasets, another potential source of bias.
当特定性别、年龄或种族特征的人群在训练数据中没有得到充分的代表时,情感识别模型可能会受到表征偏差的影响。这可能会导致错误的预测,并在敏感环境中产生个人相关性的后果。我们系统地检查了130个情感(音频、视觉和视听)数据集,发现年龄和种族背景是受影响最大的维度,而性别在情感数据集中基本平衡。所观察到的年龄和种族群体之间的差异,由于人口资料报告稀少和不一致而更加严重。最后,我们观察到缺乏关于情感数据集注释者的信息,这是另一个潜在的偏见来源。
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引用次数: 4
Two-Person Mutual Action Recognition Using Joint Dynamics and Coordinate Transformation 基于关节动力学和坐标变换的两人相互动作识别
Shian-Yu Chiu, Kun-Ru Wu, Y. Tseng
. Skeleton-based action recognition has attracted lots of attention in computer vision. Human mutual interaction recognition relies on extracting discriminative features for better understanding details. In this work, we propose two vectors to encode joint dynamics and spatial interaction information. The proposed model shows remarkable performance at handling sequential data. Experimental results demonstrate that our model outperforms state-of-the-art approaches with much less overheads.
. 基于骨骼的动作识别在计算机视觉领域引起了广泛的关注。人类交互识别依赖于提取判别特征来更好地理解细节。在这项工作中,我们提出了两个向量来编码关节动力学和空间相互作用信息。该模型在处理序列数据方面表现出显著的性能。实验结果表明,我们的模型以更少的开销优于最先进的方法。
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引用次数: 3
Ageism in AI: new forms of age discrimination in the era of algorithms and artificial intelligence 人工智能中的年龄歧视:算法和人工智能时代新形式的年龄歧视
J. Stypińska
Scholars in fairness and ethics in AI have successfully and critically identified discriminatory outcomes pertaining to the social categories of gender and race. The salient scrutiny of fairness, important for the debate of AI for social good, has nonetheless paid insufficient attention to the critical category of age. The aging population has been largely neglected during the turn to digitality and AI. Ageism in AI can be manifested in five interconnected forms: (1) age biases in algorithms and datasets, (2) age stereotypes, prejudices and ideologies of actors in AI, (3) invisibility of old age in discourses on AI, (4) discriminatory effects of use of AI technology on different age groups, (5) exclusion as users of AI technology, services and products. Furthermore, the paper provides illustrations of these forms of ageism in AI.
研究人工智能公平性和伦理的学者已经成功地、批判性地发现了与性别和种族等社会类别有关的歧视性结果。然而,对公平的显著审视对人工智能造福社会的辩论很重要,但对年龄这一关键类别的关注不够。在转向数字化和人工智能的过程中,人口老龄化在很大程度上被忽视了。人工智能中的年龄歧视可以表现为五种相互关联的形式:(1)算法和数据集中的年龄偏见;(2)人工智能参与者的年龄刻板印象、偏见和意识形态;(3)人工智能话语中老年人的隐形性;(4)使用人工智能技术对不同年龄组的歧视性影响;(5)作为人工智能技术、服务和产品的用户被排斥。此外,本文还提供了人工智能中这些形式的年龄歧视的例子。
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引用次数: 6
Informed Digital Consent for Use of AI Systems Grounded in a Model of Sexual Consent 使用基于性同意模型的人工智能系统的知情数字同意
Emmie Hine
Artificial intelligence (AI) systems shape our infospheres, mediating our interactions and defining what information we have access to. This poses a tremendous threat to individual autonomy and impacts society, both online and offline. Users are often unaware of the potential impacts of using these systems, and companies that utilise them are not incentivised to adequately inform their users of those impacts. Forms of digital design ethics, including pro-ethical design and tolerant paternalism, have been proposed to help protect user autonomy, but are not sufficient to ensure that users are educated enough to make informed decisions. In this paper, I use sexual consent as defined by American universities to outline and propose ways to implement a model of “informed digital consent” that would ensure that users are well-informed so that their autonomy is not only respected, but enhanced.
人工智能(AI)系统塑造了我们的信息圈,调解了我们的互动,并定义了我们可以访问的信息。这对个人自主权构成了巨大的威胁,并对社会产生了线上和线下的影响。用户往往不知道使用这些系统的潜在影响,而使用这些系统的公司也没有动力充分告知用户这些影响。数字设计伦理的形式,包括亲伦理设计和宽容的家长式作风,已经被提出帮助保护用户的自主权,但不足以确保用户得到足够的教育,以做出明智的决定。在本文中,我使用美国大学定义的性同意来概述并提出实施“知情数字同意”模型的方法,该模型将确保用户获得充分的信息,从而使他们的自主权不仅得到尊重,而且得到加强。
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引用次数: 0
The Ethics of Sustainability for Artificial Intelligence 人工智能的可持续性伦理
A. Owe, S. Baum
Sustainability is widely considered a good thing and is therefore a matter of ethical significance. This paper analyzes the ethical dimensions of existing work on AI and sustainability, finding that most of it is focused on sustaining the environment for human benefit. The paper calls for sustainability that is not human-centric and that extends into the distant future, especially for advanced future AI as a technology that can advance expansion beyond Earth.
可持续性被广泛认为是一件好事,因此是一个具有伦理意义的问题。本文分析了现有人工智能和可持续性工作的伦理维度,发现其中大部分都集中在为人类利益维持环境上。该报告呼吁以人类为中心的可持续发展,并延伸到遥远的未来,特别是未来先进的人工智能技术,可以推动地球以外的扩张。
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引用次数: 6
期刊
Proceedings of the 1st International Conference on AI for People: Towards Sustainable AI, CAIP 2021, 20-24 November 2021, Bologna, Italy
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