Public relations and artificial intelligence: It’s not (just) about robots

Organizations of all kinds, as well as their in-house or agency public relations teams, increasingly co-opt Artificial Intelligence (AI) to enhance their capabilities. This paper examines a relatively new topic that has received little scholarly attention: the growing relationship between AI and public relations. It outlines several key roles that AI may play in future, based on trends in other industries, and considers the implications for public relations practitioners, their clients and employers. It therefore launches a dialogue about the diversity and extent of AI’s uses in public relations practice. The paper argues that, to date, commentators have placed too much emphasis on AI’s potential for task automation; AI’s broader technological, economic and societal implications for public relations warrant greater critical attention. This does not imply that practitioners need become expert technologists; rather, they should develop a sufficient understanding of AI’s present and potential uses to be able to offer informed counsel.     

交通拥堵与雾霾污染的因果关系——基于收敛交叉映射技术的经验研究

交通拥堵与雾霾污染是制约现代城市发展的两大顽疾，准确识别交通拥堵与雾霾污染之间的交互影响，有助于城市管理者重新审视现行治堵与治霾政策的合理性。本文借助大数据平台采集了我国99个城市的高德拥堵延迟指数（CDI）、空气质量指数（AQI）及六种分项空气污染物浓度日报数据，首次采用收敛交叉映射（CCM）方法实证考察了交通拥堵与雾霾污染之间的因果关系。研究发现，CDI与AQI以及CDI与分项污染物组成的动态系统均呈现明显的非线性与弱耦合特征。基于CCM检验结果，大多数城市的CDI与AQI之间不存在显著的因果关系；从分项空气污染物的角度，大多数城市的CDI与主要空气污染物之间不存在显著因果关系，但与次要空气污染物之间却存在显著的单向或双向因果关系。上述结果表明，尽管交通拥堵与雾霾污染之间有一定关联，但在因果关系上现有的经验证据并不支持两者相互影响，治堵和治霾不能“一箭双雕”而必须“双管齐下”。本文的研究在经验上丰富了关于交通拥堵与雾霾污染交互影响的讨论，对城市管理者更加谨慎与合理地制定治堵政策与治霾政策有重要现实意义。     

大数据背景下基于社交网络的聚类随机游走抽样算法研究

数字经济时代,社交网络作为数字化平台经济的重要载体,受到了国内外学者的广泛关注。大数据背景下,社交网络的商业应用价值巨大,但由于其网络规模空前庞大,传统的网络分析方法 因计算成本过高而不再适用。而通过网络抽样算法获取样本网络,再推断整体网络,可节约计算资源, 因此抽样算法的好坏将直接影响社交网络分析结论的准确性。现有社交网络抽样算法存在忽略网络内部拓扑结构、容易陷入局部网络、抽样效率过低等缺陷。为了弥补现有社交网络抽样算法的缺陷,本文结合大数据社交网络的社区特征,提出了一种聚类随机游走抽样算法。该方法首先使用社区聚类算法将原始网络节点进行社区划分,得到多个社区网络,然后分别对每个社区进行随机游走抽样获取样本网 络。数值模拟和案例应用的结果均表明,聚类随机游走抽样算法克服了传统网络抽样算法的缺点,能够在降低网络规模的同时较好地保留原始网络的结构特征。此外,该抽样算法还可以并行运算,有效提升抽样效率,对于大数据背景下大规模社交网络的抽样实践具有重大现实意义。     

互联网免费服务价值核算研究

近年来,越来越多的企业选择以低廉或免费的价格为居民提供互联网服务,但产出价值和消费行为却无法在GDP核算中体现。服务业产出被低估一直为学术界所讨论,在此背景下,互联网经济下创新的免费商业模式对传统核算理论的挑战成为本文研究切入点。本文在“互联网免费服务与顾客价值”的易货交易框架下对其价值核算展开研究,将互联网免费服务价值核算与数据资产核算联系在一起,使数据成为连接互联网免费服务与国民经济核算中生产、消费、收入核算的桥梁。文章最后通过模拟核算案例表现了互联网免费服务核算对不同账户的影响,建议对住户部门的生产、消费与收入各增加一笔虚拟处理,对企业的生产、收入及资产也各增加一笔虚拟处理,且这6笔虚拟价值相等。     

HDDA: DataSifter: statistical obfuscation of electronic health records and other sensitive datasets

There are no practical and effective mechanisms to share high-dimensional data including sensitive information in various fields like health financial intelligence or socioeconomics without compromising either the utility of the data or exposing private personal or secure organizational information. Excessive scrambling or encoding of the information makes it less useful for modelling or analytical processing. Insufficient preprocessing may compromise sensitive information and introduce a substantial risk for re-identification of individuals by various stratification techniques. To address this problem, we developed a novel statistical obfuscation method (DataSifter) for on-the-fly de-identification of structured and unstructured sensitive high-dimensional data such as clinical data from electronic health records (EHR). DataSifter provides complete administrative control over the balance between risk of data re-identification and preservation of the data information. Simulation results suggest that DataSifter can provide privacy protection while maintaining data utility for different types of outcomes of interest. The application of DataSifter on a large autism dataset provides a realistic demonstration of its promise practical applications.     

Using logic model and visualization to conduct portfolio evaluation

Portfolio evaluation is the evaluation of multiple projects with a common purpose. While logic models have been used in many ways to support evaluation, and data visualization has been used widely to present and communicate evaluation findings, adopting logic models for portfolio evaluation and using data visualization to share findings simultaneously is surprisingly limited in the literature. With the data from a sample portfolio of 209 projects which aims to improve the system of early care and education (ECE), this study illustrated how to use logic model and data visualization techniques to conduct a portfolio evaluation by answering two evaluation questions: “To what extent are the elements of a logic model (strategies, sub-strategies, activities, outcomes, and impacts) reflected in the sample portfolio?” and “Which dominant paths through the logic model were illuminated by the data visualization technique?” For the first question, the visualization technique illuminated several dominant strategies, sub-strategies, activities, and outcomes. For the second question, our visualization techniques made it convenient to identify critical paths through the logic model. Implications for both program evaluation and program planning were discussed.     

Developing ethical approaches to data and civil society: from availability to accessibility

This research note reflects on the gaps and limitations confronting the development of ethical principles regarding the accessibility of large-scale data for civil society organizations (CSOs). Drawing upon a systematic scoping study on the use of data in the United Kingdom (UK) civil society, it finds that there are twin needs to conceptualize accessibility as more than mere availability of data, as well as examine the use of data among CSOs more generally. In order to deal with the apparent “digital divide” in UK civil society – where, despite extensive government rhetoric about data openness, organizations face not only the barriers of limited time, funds, and expertise to harness data but also the lack of representation within existing data – we present a working model in which ethical concerns accompanying data utilization by civil society may be better accounted. This suggests there is a need for further research into the nexus of civil society and data upon which interdisciplinary discussion about the ethical dimensions of engagement with data, particularly informed by insight from the social sciences, can be predicated.     

Risk‐Cost‐Benefit Analysis for Transportation Corridors with Interval Uncertainties of Heterogeneous Data

Access management, which systematically limits opportunities for egress and ingress of vehicles to highway lanes, is critical to protect trillions of dollars of current investment in transportation. This article addresses allocating resources for access management with incomplete and partially relevant data on crash rates, travel speeds, and other factors. While access management can be effective to avoid crashes, reduce travel times, and increase route capacities, the literature suggests a need for performance metrics to guide investments in resource allocation across large corridor networks and several time horizons. In this article, we describe a quantitative decision model to support an access management program via risk‐cost‐benefit analysis under data uncertainties from diverse sources of data and expertise. The approach quantifies potential benefits, including safety improvement and travel time savings, and costs of access management through functional relationships of input parameters including crash rates, corridor access point densities, and traffic volumes. Parameter uncertainties, which vary across locales and experts, are addressed via numerical interval analyses. This approach is demonstrated at several geographic scales across 7,000 kilometers of highways in a geographic region and several subregions. The demonstration prioritizes route segments that would benefit from risk management, including (i) additional data or elicitation, (ii) right‐of‐way purchases, (iii) restriction or closing of access points, (iv) new alignments, (v) developer proffers, and (vi) etc. The approach ought to be of wide interest to analysts, planners, policymakers, and stakeholders who rely on heterogeneous data and expertise for risk management.     

Carbon Emissions Abatement (CEA) allocation and compensation schemes based on DEA

As environment constraints on economic growth are strengthening, Carbon Emissions Abatement (CEA) allocation becomes a significant issue that draws academia׳s attention. In the literature, the Data Envelopment Analysis (DEA) technique has been applied to obtain CEA allocation with centralized models. Nevertheless, a centralized allocation plan suffers from an implementation difficulty in persuading decision-making units (DMUs) into an agreement. In this paper, we propose a new two-step method to mitigate this side effect. In the first step, we provide improved DEA-based centralized allocation models under the assumptions of constant returns-to-scale (CRS) and variable returns-to-scale (VRS) respectively and in the second step, two compensation schemes are developed for centralized allocation plans. An empirical application to the countries in Organization for Economic Co-operation and Development (OECD) is presented to elaborate the main idea.