Ethical Implications of Predictive Risk Intelligence

open access articleThis paper presents a case study on the ethical issues that relate to the use of Smart Information Systems (SIS) in predictive risk intelligence. The case study is based on a company that is using SIS to provide predictive risk intelligence in supply chain management (SCM), insurance, finance and sustainability. The pa-per covers an assessment of how the company recognises ethical concerns related to SIS and the ways it deals with them. Data was collected through a document review and two in-depth semi-structured interviews. Results from the case study indicate that the main ethical concerns with the use of SIS in predictive risk intelli-gence include protection of the data being used in predicting risk, data privacy and consent from those whose data has been collected from data providers such as so-cial media sites. Also, there are issues relating to the transparency and accountabil-ity of processes used in predictive intelligence. The interviews highlighted the issue of bias in using the SIS for making predictions for specific target clients. The last ethical issue was related to trust and accuracy of the predictions of the SIS. In re-sponse to these issues, the company has put in place different mechanisms to ensure responsible innovation through what it calls Responsible Data Science. Under Re-sponsible Data Science, the identified ethical issues are addressed by following a code of ethics, engaging with stakeholders and ethics committees. This paper is important because it provides lessons for the responsible implementation of SIS in industry, particularly for start-ups. The paper acknowledges ethical issues with the use of SIS in predictive risk intelligence and suggests that ethics should be a central consideration for companies and individuals developing SIS to create meaningful positive change for society

WANTED: DATA STEWARDS - (RE-)DEFINING THE ROLES AND RESPONSIBILITIES OF DATA STEWARDS FOR AN AGE OF DATA COLLABORATION

This paper is meant to inform the on-going exploration of how to enable systematic, sustainable, and responsible re-use of data through cross-sector data collaboration in the public interest (often called Data for Good). Data stewards build trust between organizations, agilely creating relationships between leaders from different sectors and backgrounds.Specifically, the position paper seeks to outline the roles and responsibilities of the emergent data steward profession. It is intended to support data-holding businesses and public institutions to create and promote data stewards in the public and private sectors; and to establish a network of these data stewards—as recently recommended by the High Level Expert Group to the European Commission on Business-to-Government Data Sharing.

Recruitment, fundraising and interactivity through social media

Abstract Recruitment, fundraising and interactivity through social media This paper will interrogate these areas and look at the existing literature & comments in relation to how the concept of politicians as brands may require a re-think. I hope to achieve this by applying current digital marketing concepts to Obama's use of digital media (in particular WEB 2.0). This includes word of mouth, permission marketing, and online advocacy. Online we can and do act as brand ambassadors – a key aspect of Web 2.0 or social media is the process whereby the exchange of data creates networks of trust. Online we ask, question and receive data; depending on its use value this is then converted into information by us as consumers. Online advertising and PR have morphed into conversations and narratives about products, services, experiences and customer service. Brands are dead … long live brands

Locating Ethics in Data Science: Responsibility and Accountability in Global and Distributed Knowledge Production

This is the author accepted manuscript. The final version is available from Royal Society via the DOI in this record.The distributed and global nature of data science creates challenges for evaluating the quality, import and potential impact of the data and knowledge claims being produced. This has significant consequences for the management and oversight of responsibilities and accountabilities in data science. In particular, it makes it difficult to determine who is responsible for what output, and how such responsibilities relate to each other; what ‘participation’ means and which accountabilities it involves, with regards to data ownership, donation and sharing as well as data analysis, re-use and authorship; and whether the trust placed on automated tools for data mining and interpretation is warranted (especially since data processing strategies and tools are often developed separately from the situations of data use where ethical concerns typically emerge). To address these challenges, this paper advocates a participative, reflexive management of data practices. Regulatory structures should encourage data scientists to examine the historical lineages and ethical implications of their work at regular intervals. They should also foster awareness of the multitude of skills and perspectives involved in data science, highlighting how each perspective is partial and in need of confrontation with others. This approach has the potential to improve not only the ethical oversight for data science initiatives, but also the quality and reliability of research outputs.This research was funded by the European Research Council grant award 335925 (“The Epistemology of Data-Intensive Science”), the Leverhulme Trust Grant number RPG-2013- 153 and the Australian Research Council, Discovery Project DP160102989

The usage of social media and e-reputation system in global supply chain : comparative cases from diamond & automotive industries

The last decade witnesses a heave use of social media-based information systems in different fields of business such as logistics, procurement, and supply chain management. Managing these types of information systems, could help companies to outsource their supply chain functions in a global scale and enhance their competitive advantages. However, the digital performance of these activities inherent risks of inappropriate supplier selection process, lack of trust, limited information about supply conditions (e.g., pricing, shipping and timing). To address such challenges, this research explains how companies use e-reputation systems and social media to select their global trusted suppliers. Based on two-case evidence from British Diamond and Egyptian Automotive companies, the researchers conducted 20 interviews with purchasing and supply chain professionals. Chen & Lin’s reputation system model has been adopted to explain the process of selecting and evaluating a trusted supplier and to inform our data analysis. Our findings pointed diamond professionals’ lack of experience of how to use e-reputation systems and lack do not understand the role of social media-based ratings or referrals during the stages of selection suppliers’ discovery and approval. Though, automotive professionals find e-reputation system a strong tool to build goodwill, tacit credibility, competence and predictable trust. Ironically, both cases confirm that supply chain professional use these systems to re-evaluate and reselect their existing suppliers than to extend new supply networks. Keywords: e-Reputation Systems, Social Media, Global Supply Chai

Social network analysis shows direct evidence for social transmission of tool use in wild chimpanzees

The authors are grateful to the Royal Zoological Society of Scotland for providing core funding for the Budongo Conservation Field Station. The fieldwork of CH was funded by the Leverhulme Trust, the Lucie Burgers Stichting, and the British Academy. TP was funded by the Canadian Research Chair in Continental Ecosystem Ecology, and received computational support from the Theoretical Ecosystem Ecology group at UQAR. The research leading to these results has received funding from the People Programme (Marie Curie Actions) and from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007–2013) REA grant agreement n°329197 awarded to TG, ERC grant agreement n° 283871 awarded to KZ. WH was funded by a BBSRC grant (BB/I007997/1).Social network analysis methods have made it possible to test whether novel behaviors in animals spread through individual or social learning. To date, however, social network analysis of wild populations has been limited to static models that cannot precisely reflect the dynamics of learning, for instance, the impact of multiple observations across time. Here, we present a novel dynamic version of network analysis that is capable of capturing temporal aspects of acquisition-that is, how successive observations by an individual influence its acquisition of the novel behavior. We apply this model to studying the spread of two novel tool-use variants, "moss-sponging'' and "leaf-sponge re-use,'' in the Sonso chimpanzee community of Budongo Forest, Uganda. Chimpanzees are widely considered the most "cultural'' of all animal species, with 39 behaviors suspected as socially acquired, most of them in the domain of tool-use. The cultural hypothesis is supported by experimental data from captive chimpanzees and a range of observational data. However, for wild groups, there is still no direct experimental evidence for social learning, nor has there been any direct observation of social diffusion of behavioral innovations. Here, we tested both a static and a dynamic network model and found strong evidence that diffusion patterns of moss-sponging, but not leaf-sponge re-use, were significantly better explained by social than individual learning. The most conservative estimate of social transmission accounted for 85% of observed events, with an estimated 15-fold increase in learning rate for each time a novice observed an informed individual moss-sponging. We conclude that group-specific behavioral variants in wild chimpanzees can be socially learned, adding to the evidence that this prerequisite for culture originated in a common ancestor of great apes and humans, long before the advent of modern humans.Publisher PDFPeer reviewe

Global Community Guidelines for Documenting, Sharing, and Reusing Quality Information of Individual Digital Datasets

Open-source science builds on open and free resources that include data, metadata, software, and workflows. Informed decisions on whether and how to (re)use digital datasets are dependent on an understanding about the quality of the underpinning data and relevant information. However, quality information, being difficult to curate and often context specific, is currently not readily available for sharing within and across disciplines. To help address this challenge and promote the creation and (re)use of freely and openly shared information about the quality of individual datasets, members of several groups around the world have undertaken an effort to develop international community guidelines with practical recommendations for the Earth science community, collaborating with international domain experts. The guidelines were inspired by the guiding principles of being findable, accessible, interoperable, and reusable (FAIR). Use of the FAIR dataset quality information guidelines is intended to help stakeholders, such as scientific data centers, digital data repositories, and producers, publishers, stewards and managers of data, to: i) capture, describe, and represent quality information of their datasets in a manner that is consistent with the FAIR Guiding Principles; ii) allow for the maximum discovery, trust, sharing, and reuse of their datasets; and iii) enable international access to and integration of dataset quality information. This article describes the processes that developed the guidelines that are aligned with the FAIR principles, presents a generic quality assessment workflow, describes the guidelines for preparing and disseminating dataset quality information, and outlines a path forward to improve their disciplinary diversity

Information-seeking on the Web with Trusted Social Networks - from Theory to Systems

This research investigates how synergies between the Web and social networks can enhance the process of obtaining relevant and trustworthy information. A review of literature on personalised search, social search, recommender systems, social networks and trust propagation reveals limitations of existing technology in areas such as relevance, collaboration, task-adaptivity and trust. In response to these limitations I present a Web-based approach to information-seeking using social networks. This approach takes a source-centric perspective on the information-seeking process, aiming to identify trustworthy sources of relevant information from within the user's social network. An empirical study of source-selection decisions in information- and recommendation-seeking identified five factors that influence the choice of source, and its perceived trustworthiness. The priority given to each of these factors was found to vary according to the criticality and subjectivity of the task. A series of algorithms have been developed that operationalise three of these factors (expertise, experience, affinity) and generate from various data sources a number of trust metrics for use in social network-based information seeking. The most significant of these data sources is Revyu.com, a reviewing and rating Web site implemented as part of this research, that takes input from regular users and makes it available on the Semantic Web for easy re-use by the implemented algorithms. Output of the algorithms is used in Hoonoh.com, a Semantic Web-based system that has been developed to support users in identifying relevant and trustworthy information sources within their social networks. Evaluation of this system's ability to predict source selections showed more promising results for the experience factor than for expertise or affinity. This may be attributed to the greater demands these two factors place in terms of input data. Limitations of the work and opportunities for future research are discussed