Decision-Making in Driver-Automation Shared Control:A Review and Perspectives

Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to control.The human driver,as an essential agent in the driver-vehicle shared control systems,should be precisely modeled regarding their cognitive processes,control strategies,and decision-making processes.The interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of humans.Many open-ended questions arise,such as what proper role of human drivers should act in a shared control scheme?How to make an intelligent decision capable of balancing the benefits of agents in shared control systems?Due to the advent of these attentions and questions,it is desirable to present a survey on the decision making between human drivers and highly automated vehicles,to understand their architectures,human driver modeling,and interaction strategies under the driver-vehicle shared schemes.Finally,we give a further discussion on the key future challenges and opportunities.They are likely to shape new potential research directions.

Crossing the Achilles Heel of Algorithms:Identifying the Developmental Dilemma of Artificial Intelligence-Assisted Judicial Decision-Making

In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to fundamentally improve.This can be considered a true challenge in the realm of AI-assisted judicial decision-making.By examining the court’s acceptance,integration,and trade-offs of AI technology embedded in the judicial field,the exploration of potential conflicts,interactions,and even mutual shaping between the two will not only reshape their conceptual connotations and intellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of the judicial trial system.

Digital Disparities:How Artificial Intelligence Can Facilitate Anti-Black Racism in the U.S.Healthcare Sector

This paper delves into the intricate interplay between artificial intelligence(AI)systems and the perpetuation of Anti-Black racism within the United States medical industry.Despite the promising potential of AI to enhance healthcare outcomes and reduce disparities,there is a growing concern that these technologies may inadvertently/advertently exacerbate existing racial inequalities.Focusing specifically on the experiences of Black patients,this research investigates how the following AI components:medical algorithms,machine learning,and natural learning processes are contributing to the unequal distribution of medical resources,diagnosis,and health care treatment of those classified as Black.Furthermore,this review employs a multidisciplinary approach,combining insights from computer science,medical ethics,and social justice theory to analyze the mechanisms through which AI systems may encode and reinforce racial biases.By dissecting the three primary components of AI,this paper aims to present a clear understanding of how these technologies work,how they intersect,and how they may inherently perpetuate harmful stereotypes resulting in negligent outcomes for Black patients.Furthermore,this paper explores the ethical implications of deploying AI in healthcare settings and calls for increased transparency,accountability,and diversity in the development and implementation of these technologies.Finally,it is important that I prefer the following paper with a clear and concise definition of what I refer to as Anti-Black racism throughout the text.Therefore,I assert the following:Anti-Black racism refers to prejudice,discrimination,or antagonism directed against individuals or communities of African descent based on their race.It involves the belief in the inherent superiority of one race over another and the systemic and institutional practices that perpetuate inequality and disadvantage for Black people.Furthermore,I proclaim that this form of racism can be manifested in various ways,such as unequal access to opportunities,resources,education,employment,and fair treatment within social,economic,and political systems.It is also pertinent to acknowledge that Anti-Black racism is deeply rooted in historical and societal structures throughout the U.S.borders and beyond,leading to systemic disadvantages and disparities that impact the well-being and life chances of Black individuals and communities.Addressing Anti-Black racism involves recognizing and challenging both individual attitudes and systemic structures that contribute to discrimination and inequality.Efforts to combat Anti-Black racism include promoting awareness,education,advocacy for policy changes,and fostering a culture of inclusivity and equality.

配网用户侧环网及自动化方案的探讨

对城市10 kV电缆配电网的常用接线方式进行了分析,提出了10 kV用户侧实现电缆环网的方案。经过比较,用户侧环网接线方式的技术经济效果最好。然后针对用户侧电缆环网的特点,提出了用户室的自动化重点和功能配置。最后对电缆配网的运行管理方式提出了建议。

Framework of automated value stream mapping for lean production under the Industry 4.0 paradigm

For efficient use of value stream mapping(VSM)for multi-varieties and small batch production in a data-rich environment enabled by Industry 4.0 technologies,a systematic framework of VSM to rejuvenate traditional lean tools is proposed.It addresses the issue that traditional VSM requires intensive on-site investigation and replies on experience,which hinders decisionmaking efficiency in dynamic and complex environments.The proposed framework follows the data-information-knowledge hierarchy model,and demonstrates how data can be collected in a production workshop,processed into information,and then interpreted into knowledge.In this paper,the necessity and limitations of VSM in automated root cause analysis are first discussed,with a literature review on lean production tools,especially VSM and VSM-based decision making in Industry 4.0.An implementation case of a furniture manufacturer in China is presented,where decision tree algorithm was used for automated root cause analysis.The results indicate that automated VSM can make good use of production data to cater for multi-varieties and small batch production with timely on-site waste identification and analysis.The proposed framework is also suggested as a guideline to renew other lean tools for reliable and efficient decision-making.

Fiduciary Responsibility: Facilitating Public Trust in Automated Decision Making

Automated decision-making systems are being increasingly deployed and affect the public in a multitude of positive and negative ways.Governmental and private institutions use these systems to process information according to certain human-devised rules in order to address social problems or organizational challenges.Both research and real-world experience indicate that the public lacks trust in automated decision-making systems and the institutions that deploy them.The recreancy theorem argues that the public is more likely to trust and support decisions made or influenced by automated decision-making systems if the institutions that administer them meet their fiduciary responsibility.However,often the public is never informed of how these systems operate and resultant institutional decisions are made.A“black box”effect of automated decision-making systems reduces the public’s perceptions of integrity and trustworthiness.Consequently,the institutions administering these systems are less able to assess whether the decisions are just.The result is that the public loses the capacity to identify,challenge,and rectify unfairness or the costs associated with the loss of public goods or benefits.The current position paper defines and explains the role of fiduciary responsibility within an automated decision-making system.We formulate an automated decision-making system as a data science lifecycle(DSL)and examine the implications of fiduciary responsibility within the context of the DSL.Fiduciary responsibility within DSLs provides a methodology for addressing the public’s lack of trust in automated decision-making systems and the institutions that employ them to make decisions affecting the public.We posit that fiduciary responsibility manifests in several contexts of a DSL,each of which requires its own mitigation of sources of mistrust.To instantiate fiduciary responsibility,a Los Angeles Police Department(LAPD)predictive policing case study is examined.We examine the development and deployment by the LAPD of predictive policing technology and identify several ways in which the LAPD failed to meet its fiduciary responsibility.

Utilizing Artificial Intelligence (AI) for the Identification and Management of Marine Protected Areas (MPAs): A Review

The article discusses the application of artificial intelligence (AI) and automation in marine conservation, specifically in relation to the protection of marine ecosystems and the definition of marine protected areas (MPAs). It highlights the threats that marine ecosystems face due to human activities and emphasizes the importance of effective management and conservation efforts. By improving data gathering, processing, monitoring, and analysis, artificial intelligence, and automation, they can revolutionize marine research. In conclusion, this study emphasizes the importance of AI and automation in marine conservation responsibly and ethically. In order to integrate these technologies into decision-making processes, stakeholders and marine conservation professionals must collaborate. Through the use of artificial intelligence and automation, marine conservation efforts can be transformed by establishing new methods of collecting and analyzing data, making informed decisions, and managing marine ecosystems.

PLATFORM SELECTION FOR COMPLEX SYSTEMS:BUILDING AUTOMATION SYSTEMS

Automation systems for buildings interconnect components and technologies from the information technology industry and the telecommunications industry.In these industries,existing platforms and new platforms（that are designed to make building automation systems work） compete for market acceptance and consequently several platform battles among suppliers for building automation networking are being waged.It is unclear what the outcome of these battles will be and also which factors are important in achieving platform dominance.Taking the fuzziness of decision makers＇ judgments into account,a fuzzy multi-criteria decision-making methodology called the Fuzzy Analytic Hierarchy Process is applied to investigate the importance of such factors in platform battles for building automation networking.We present the relative importance of the factors for three types of platforms（subsystem platforms,system platforms,and evolved subsystem platforms）.The results provide a first indication that the set of important factors differs per type of platform.For example,when focusing on other stakeholders,for subsystem platforms,the previous installed base is of importance;for system platforms,the diversity of the network of stakeholders is essential;and for evolved subsystem platforms,the judiciary is an important factor.

完全自动化决策拒绝权之正当性及其实现路径——以《个人信息保护法》第24条第3款为中心

完全自动化决策更易引发系统性风险,对隐私和公平造成威胁。欧盟立法以“人在环路”作为防范自动化决策系统滥用危害的基本遵循,对完全自动化决策一般禁止、例外允许。“人在环路”应被解释为信息处理者的干预义务,而非信息主体的权利,利于个体正义的实现和责任主体的明确。我国立法规定了完全自动化决策拒绝权,信息主体可行使请求权以迂回实现“人在环路”之法律效果,亦避免给处理者过重义务,对人工智能产业的发展造成不合理的阻碍。该项权利并非事后救济权,而是信息主体在决策的事前、事中及事后的“个人参与”,应满足“仅通过自动化决策的方式作出决定”和“对个人权益有重大影响”两个要件,且需与信息主体的其他权利配合行权。