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From Pluralistic Normative Principles to Autonomous-Agent Rules 从多元规范原则到自治主体规则
IF 7.4 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-10-29 DOI: 10.1007/s11023-022-09614-w
B. Townsend, Colin Paterson, T. Arvind, G. Nemirovsky, R. Calinescu, Ana Cavalcanti, I. Habli, Alan Thomas
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引用次数: 8
Meta’s Oversight Board: A Review and Critical Assessment Meta的监督委员会:审查和关键评估
IF 7.4 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-10-24 DOI: 10.1007/s11023-022-09613-x
David Wong, L. Floridi
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引用次数: 8
The End of Vagueness: Technological Epistemicism, Surveillance Capitalism, and Explainable Artificial Intelligence 模糊的终结:技术认识论、监视资本主义和可解释的人工智能
IF 7.4 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-09-01 DOI: 10.1007/s11023-022-09609-7
Alison Duncan Kerr, Kevin Scharp
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引用次数: 1
The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: what can they learn from each other? 美国《2022年算法问责法案》与欧盟《人工智能法案》:他们可以相互学习什么?
IF 7.4 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-08-18 DOI: 10.1007/s11023-022-09612-y
Jakob Mökander, Prathm Juneja, David Watson, Luciano Floridi
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引用次数: 24
Contestable AI by Design: Towards a Framework 可竞争的人工智能设计:走向一个框架
IF 7.4 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-08-13 DOI: 10.1007/s11023-022-09611-z
K. Alfrink, I. Keller, Gerd Kortuem, N. Doorn
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引用次数: 11
Realising Meaningful Human Control Over Automated Driving Systems: A Multidisciplinary Approach. 实现人类对自动驾驶系统的有效控制:多学科方法。
IF 7.4 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-07-28 DOI: 10.1007/s11023-022-09608-8
Filippo Santoni de Sio, Giulio Mecacci, Simeon Calvert, Daniel Heikoop, Marjan Hagenzieker, Bart van Arem

The paper presents a framework to realise "meaningful human control" over Automated Driving Systems. The framework is based on an original synthesis of the results of the multidisciplinary research project "Meaningful Human Control over Automated Driving Systems" lead by a team of engineers, philosophers, and psychologists at Delft University of the Technology from 2017 to 2021. Meaningful human control aims at protecting safety and reducing responsibility gaps. The framework is based on the core assumption that human persons and institutions, not hardware and software and their algorithms, should remain ultimately-though not necessarily directly-in control of, and thus morally responsible for, the potentially dangerous operation of driving in mixed traffic. We propose an Automated Driving System to be under meaningful human control if it behaves according to the relevant reasons of the relevant human actors (tracking), and that any potentially dangerous event can be related to a human actor (tracing). We operationalise the requirements for meaningful human control through multidisciplinary work in philosophy, behavioural psychology and traffic engineering. The tracking condition is operationalised via a proximal scale of reasons and the tracing condition via an evaluation cascade table. We review the implications and requirements for the behaviour and skills of human actors, in particular related to supervisory control and driver education. We show how the evaluation cascade table can be applied in concrete engineering use cases in combination with the definition of core components to expose deficiencies in traceability, thereby avoiding so-called responsibility gaps. Future research directions are proposed to expand the philosophical framework and use cases, supervisory control and driver education, real-world pilots and institutional embedding.

本文介绍了一个实现对自动驾驶系统 "有意义的人类控制 "的框架。该框架基于对代尔夫特理工大学工程师、哲学家和心理学家团队于 2017 年至 2021 年开展的多学科研究项目 "对自动驾驶系统进行有意义的人类控制 "成果的原创性综合。有意义的人类控制旨在保护安全和减少责任差距。该框架基于以下核心假设:人类和机构,而非硬件和软件及其算法,应最终--虽然不一定是直接--控制混合交通中潜在危险的驾驶操作,并因此承担道义责任。我们认为,如果自动驾驶系统的行为符合相关人类行为者的相关原因(跟踪),并且任何潜在的危险事件都与人类行为者有关(追踪),那么该系统就处于人类的有效控制之下。我们通过哲学、行为心理学和交通工程学等多学科的研究,将有意义的人类控制要求具体化。跟踪条件通过近因量表来操作,追踪条件通过评估级联表来操作。我们回顾了对人类行为者的行为和技能的影响和要求,特别是与监督控制和驾驶员教育相关的影响和要求。我们展示了如何将评估级联表应用于具体的工程用例,并结合核心组件的定义来揭示可追溯性的缺陷,从而避免所谓的责任缺口。我们提出了未来的研究方向,以扩展哲学框架和使用案例、监督控制和驾驶员教育、现实世界试点和机构嵌入。
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引用次数: 0
Causal and Evidential Conditionals 因果条件和证据条件
IF 7.4 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-07-11 DOI: 10.1007/s11023-022-09606-w
M. Günther
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引用次数: 1
Defining Explanation and Explanatory Depth in XAI XAI中解释和解释深度的定义
IF 7.4 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-06-29 DOI: 10.1007/s11023-022-09607-9
Stefan Buijsman
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引用次数: 7
Assembled Bias: Beyond Transparent Algorithmic Bias 集合偏见:超越透明的算法偏见
IF 7.4 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-06-18 DOI: 10.1007/s11023-022-09605-x
R. Waller, Russell L. Waller
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引用次数: 1
Ethics of Self-driving Cars: A Naturalistic Approach 自动驾驶汽车的伦理:一种自然主义的方法
IF 7.4 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-06-09 DOI: 10.1007/s11023-022-09604-y
Selene Arfini, D. Spinelli, D. Chiffi
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引用次数: 4
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Minds and Machines
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