The ethical use of artificial intelligence in human resource management: a decision-making framework

Ethics and Information Technology - Artificial intelligence (AI) is increasingly inputting into various human resource management (HRM) functions, such as sourcing job applicants and selecting...     

Human-aligned artificial intelligence is a multiobjective problem

As the capabilities of artificial intelligence (AI) systems improve, it becomes important to constrain their actions to ensure their behaviour remains beneficial to humanity. A variety of ethical, legal and safety-based frameworks have been proposed as a basis for designing these constraints. Despite their variations, these frameworks share the common characteristic that decision-making must consider multiple potentially conflicting factors. We demonstrate that these alignment frameworks can be represented as utility functions, but that the widely used Maximum Expected Utility (MEU) paradigm provides insufficient support for such multiobjective decision-making. We show that a Multiobjective Maximum Expected Utility paradigm based on the combination of vector utilities and non-linear action–selection can overcome many of the issues which limit MEU’s effectiveness in implementing aligned AI. We examine existing approaches to multiobjective AI, and identify how these can contribute to the development of human-aligned intelligent agents.     

Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management

This paper presents a vision for a Disaster City Digital Twin paradigm that can: (i) enable interdisciplinary convergence in the field of crisis informatics and information and communication technology (ICT) in disaster management; (ii) integrate artificial intelligence (AI) algorithms and approaches to improve situation assessment, decision making, and coordination among various stakeholders; and (iii) enable increased visibility into network dynamics of complex disaster management and humanitarian actions. The number of humanitarian relief actions is growing due to the increased frequency of natural and man-made crises. Various streams of research across different disciplines have focused on ICT and AI solutions for enhancing disaster management processes. However, most of the existing research is fragmented without a common vision towards a converging paradigm. Recognizing this, this paper presents the Disaster City Digital Twin as a unifying paradigm. The four main components of the proposed Digital Twin paradigm include: multi-data sensing for data collection, data integration and analytics, multi-actor game-theoretic decision making, and dynamic network analysis. For each component, the current state of the art related to AI methods and approaches are examined and gaps are identified.     

人工情感与人工智能应用研究

生活世界中人工情感研发得到快速发展,但还不能比及人类情感。语境残破和缺身涉入是人工情感不及人类情感真挚、真切的两个重要原因。建构语境与虚拟身体的协同创新有助于增强人工情感的逼真度。人工情感在教育、客服、营销、制造、安防等领域的应用,将有助于提高人机交互的有效性、体验性和真实性。随着人工情感的增强,我们既要积极支持,同时也要做好应对因人工情感所带来各种风险的防控。     

A perspective on natural and artificial intelligence

Abstract

Intelligence is an attribute that has, since time immemorial, drawn the line of distinction between man and machine. Artificial Intelligence (AI) refers to the endeavor of the former to introduce some of this special faculty into the latter. Just as natural intelligence has undergone major changes as regards its definitions and understanding, so has the field of AI. In order to encompass the gamut of this fundamental capability and know its origins, AI researchers have often had to deal with psychological and philosophical viewpoints on the issue. From the point of view of cognitive psychology, the Information Processing (IP) paradigm and IP systems are of special interest, and we present a brief overview of these topics. While the AI community claims to have some understanding of the concept of knowledge, the idea of consciousness, which we consider of finer grain than any other, has received little practical attention. These related terms are discussed at length in the article. Further, of late there has been a movement toward incorporating a background of common‐sense reasoning in AI systems. We emphasize the importance of this trend, especially in distributed AI. The basics of adaptability and learning are also discussed. We sum up the ideas presented and link them to the current progress in AI research with specifics aimed at making it more dynamic.     

Fashion analysis and understanding with artificial intelligence

As handling fashion big data with Artificial Intelligence (AI) has become exciting challenges for computer scientists, fashion studies have received increasing attention in computer vision, machine learning and multimedia communities in the past few years. In this paper, introduce the progress in fashion research and provide a taxonomy of these fashion studies that include low-level fashion recognition, middle-level fashion understanding and high-level fashion applications. Finally, we discuss the challenges that when the fashion industry faces AI technologies.     

人工智能伦理的国际软法之治：现状、挑战与对策

人工智能技术不仅能快速赋能经济社会发展,也可能引发诸多与人工智能技术本身特征和发展高度相关的伦理问题。国际软法因具有灵活高效、适用成本低,能填补硬法空白,以及方便区分治理、分层应对伦理问题的优势,其在人工智能伦理治理领域的勃兴几乎是必然的。在该领域国际软法发达、硬法落后的现状下,面对国际软法主体间合作不稳定、有时得不到有效实施的治理挑战,治理模式逐渐向软硬兼备、软法“硬化”转变,以提高软法约束力与执行可能性。建议构造国际软硬法混合治理的“中心—外围”模式、构建间接执行机制,以完善人工智能伦理的国际软法治理对策。     

Consumer engagement via interactive artificial intelligence and mixed reality

The use of immersive technologies has changed the consumption environment in which retailers provide services. We present findings from a study designed to investigate consumer responses toward a $17 million AI-embedded mixed reality (MR) exhibit in a retail/entertainment complex which combines advanced technology entertainment with retail shopping. Findings from our study demonstrate that the quality of AI (i.e., speech recognition and synthesis via machine learning) associated with an augmented object increases MR immersion associated with spatial immersion, MR enjoyment, and consumers’ perceptions of novel experiences. Collectively, these increase consumer engagement, and positively influence behavioral responses—specifically, purchase intentions and intentions to share experiences with social groups. Overall, findings from this study show that interactive AI and MR technology open new avenues to promote consumer engagement.     

Business Innovation based on artificial intelligence and Blockchain technology

The growing business evolution and the latest Artificial Intelligence (AI) make the different business practices to be enhanced by the ability to create new means of collaboration. Such growing technology helps to deliver brand services and even some new kinds of corporate interactions with customers and staff. AI digitization simultaneously emphasized businesses to focus on the existing strategies and regularly and early pursue new market opportunities. While digital technology research in the framework of business innovation is gaining greater interest and the privacy of data can be maintained by Blockchain technology. Therefore in this paper, Business Innovation based on artificial intelligence and Blockchain technology (BI-AIBT) has been proposed to enhance the business practices and maintain the secured interaction among the various clients. The collection of qualitative empirical data is made up of few primary respondents from two distinct business sectors. BI-AIBT has been evaluated by undertaking and exploring the difference and similarities between digitalization's impact on value development, proposal, and business capture. Besides, organizational capacities and staff skills interaction issues can be improved by BT. The experimental result suggests that digital transformation is usually regarded as essential and improves business innovation strategies. The numerical result proposed BI-AIBT improves the demand prediction ratio (97.1%), product quality ratio (98.3%), Business development ratio (98.9%), customer behavior analysis ratio (96.3%), and customer satisfaction ratio (97.2%).     

Anticipatory innovation of professional services: The case of auditing and artificial intelligence

With the rise of artificial intelligence (AI), professional services firms (PSFs) need to innovate their services to adapt to AI. However, traditional ad hoc innovations driven by individual professionals have limitations in incorporating new technology outside their expertise. Although service R&D—an organizational function for centralized coordination of service innovations in strategically targeted areas—is potentially effective, studies on service R&D have still been scarce. This case study aims to fill the gap by examining how PSFs can establish and utilize service R&D to innovate services, overcoming the challenges of AI adoption. An in-depth qualitative study was conducted on the process by which the Big Four audit firms incorporated AI into their external audit service in Japan in the 2010s. The analysis shows the detailed process of how newly created service R&D organizations advanced AI adoption in the case firms. This study contributes to the literature on innovations in services and PSFs by (1) demonstrating the neglected but critical role of service R&D as an innovation enabler beyond the existing expertise of service firms, (2) constructing a three-phase model of the evolution of the service R&D function, and (3) suggesting the significance of innovation process design for the legitimation of innovations. This study also expands our knowledge of AI adoption, presenting a process tailored to address the challenges inherent in AI adoption for PSFs.     

以色列人工智能发展现状分析及政策举措

目前,世界上许多国家都在竞相发展人工智能,以创新创业见长的以色列在人工智能方面也作出了重要部署,并有突出的表现.本文基于以色列创新署发布的《2019年创新报告》等报告及数据,着重分析了以色列近年来在人工智能方面取得的成就和当前发展所面临的障碍,以及以色列为保持现有优势并进一步推动人工智能发展的政策举措,旨在为我国人工智能的发展提供参考借鉴.     

Close, but not the same: Locally headquartered organizations and agglomeration economies in a declining industry

Departing from research on expanding, high-technology industries, we study the impact of agglomeration in a declining, low-technology industry. The setting is U.S. footwear manufacturing between 1975 and 1991, when import competition rendered local support critical for survival. We examine how agglomeration-related survival benefits depended upon the presence of locally headquartered manufacturing plants and whether such benefits came at the expense of other local industries. Consistent with ecological arguments, plant failure rates were higher in agglomerations but this effect was attenuated and, in some cases, reversed in agglomerations with more locally headquartered plants. Moreover, only locally headquartered plants experienced such benefits; remotely headquartered plants failed at higher rates in agglomerations. Although more footwear manufacturing jobs were retained in agglomerations with many locally headquartered plants, such locales also exhibited lower manufacturing job growth in other industries. These findings lend greater generalizability to agglomeration theories and also imply trade-offs at the community level.     

Market power and artificial intelligence work on online labour markets

We investigate three alternative but complementary indicators of market power on one of the largest online labour markets (OLMs) in Europe: (1) the elasticity of labour demand, (2) the elasticity of labour supply, and (3) the concentration of market shares. We explore how these indicators relate to an exogenous change in platform policy. In the middle of the observation period, the platform made it mandatory for employers to signal the rates they were willing to pay as given by the level of experience required to perform a project, i.e., entry, intermediate or expert level. We find a positive labour supply elasticity ranging between 0.06 and 0.15, which is higher for expert-level projects. We also find that the labour demand elasticity increased while the labour supply elasticity decreased after the policy change. Based on this, we argue that market-designing platform providers can influence the labour demand and supply elasticities on OLMs with the terms and conditions they set for the platform. We also explore the demand for and supply of AI-related labour on the OLM under study. We provide evidence for a significantly higher demand for AI-related labour (ranging from +1.4% to +4.1%) and a significantly lower supply of AI-related labour (ranging from -6.8% to -1.6%) than for other types of labour. We also find that workers on AI projects receive 3.0%-3.2% higher wages than workers on non-AI projects.     

China's catching-up in artificial intelligence seen as a co-evolution of corporate and national innovation systems

Inspired by Christopher Freeman's work on how radical technical change opens up for shifts in world leadership and on the role of innovation systems in this process, this paper explores China's emergence as a lead country in artificial intelligence as reflecting a co-evolution of Corporate and National Innovation Systems. Taking Freeman's (1987) work on Japan as our lead, we focus on the domestic interaction within and on the openness of China's national innovation system. To follow up on his prediction of the increasing importance of big companies as network leaders, we introduce the concept “corporate innovation system” with special attention to two Chinese tech giants: Alibaba and Tencent.