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\"The Global Diamond Industry: Economics and Development brings together a collection of papers covering various aspects of the diamond industry including economics, law, history, sociology and development. These volumes are motivated by one objective alone and that is to provide intellectual light where none exists. The diamond industry is one that is long steeped in secrecy and each chapter and even each well researched anecdote helps others to understand this commodity and what are at times mysterious operations of the industry. This first volume presents literature tackling broad issues around the structure of the industry and demand and pricing of diamonds\"-- Provided by publisher.

Predictive analytics, or the use of electronic algorithms to forecast future events in real time, makes it possible to harness the power of big data to improve the health of patients and lower the cost of health care. However, this opportunity raises policy, ethical, and legal challenges. In this article we analyze the major challenges to implementing predictive analytics in health care settings and make broad recommendations for overcoming challenges raised in the four phases of the life cycle of a predictive analytics model: acquiring data to build the model, building and validating it, testing it in real-world settings, and disseminating and using it more broadly. For instance, we recommend that model developers implement governance structures that include patients and other stakeholders starting in the earliest phases of development. In addition, developers should be allowed to use already collected patient data without explicit consent, provided that they comply with federal regulations regarding research on human subjects and the privacy of health information.

Research ethics has traditionally been guided by well-established documents such as the Belmont Report and the Declaration of Helsinki. At the same time, the introduction of Big Data methods, that is having a great impact in behavioral research, is raising complex ethical issues that make protection of research participants an increasingly difficult challenge. By conducting 39 semi-structured interviews with academic scholars in both Switzerland and United States, our research aims at exploring the code of ethics and research practices of academic scholars involved in Big Data studies in the fields of psychology and sociology to understand if the principles set by the Belmont Report are still considered relevant in Big Data research. Our study shows how scholars generally find traditional principles to be a suitable guide to perform ethical data research but, at the same time, they recognized and elaborated on the challenges embedded in their practical application. In addition, due to the growing introduction of new actors in scholarly research, such as data holders and owners, it was also questioned whether responsibility to protect research participants should fall solely on investigators. In order to appropriately address ethics issues in Big Data research projects, education in ethics, exchange and dialogue between research teams and scholars from different disciplines should be enhanced. In addition, models of consultancy and shared responsibility between investigators, data owners and review boards should be implemented in order to ensure better protection of research participants.

The creatures at the bottom of the ocean are little-studied, but emerging data hint at long-term damage from efforts to harvest metals on the sea floor. The creatures at the bottom of the ocean are little-studied, but emerging data hint at long-term damage from efforts to harvest metals on the sea floor.

New Caledonia is a global biodiversity hotspot facing extreme environmental degradation. Given the urgent need for conservation prioritisation, we have made a first-pass quantitative assessment of the distribution of Narrow Endemic Species (NES) in the flora to identify species and sites that are potentially important for conservation action. We assessed the distributional status of all angiosperm and gymnosperm species using data from taxonomic descriptions and herbarium samples. We characterised species as being NES if they occurred in 3 or fewer locations. In total, 635 of the 2930 assessed species were classed as NES, of which only 150 have been subjected to the IUCN conservation assessment. As the distributional patterns of un-assessed species from one or two locations correspond well with assessed species which have been classified as Critically Endangered or Endangered respectively, we suggest that our distributional data can be used to prioritise species for IUCN assessment. We also used the distributional data to produce a map of \"Hotspots of Plant Narrow Endemism\" (HPNE). Combined, we used these data to evaluate the coincidence of NES with mining activities (a major source of threat on New Caledonia) and also areas of conservation protection. This is to identify species and locations in most urgent need of further conservation assessment and subsequent action. Finally, we grouped the NES based on the environments they occurred in and modelled the habitat distribution of these groups with a Maximum Entropy Species Distribution Model (MaxEnt). The NES were separable into three different groups based primarily on geological differences. The distribution of the habitat types for each group coincide partially with the HPNE described above and also indicates some areas which have high habitat suitability but few recorded NES. Some of these areas may represent under-sampled hotspots of narrow endemism and are priorities for further field work.

The rapid development of computer vision technologies and applications has brought forth a range of social and ethical challenges. Due to the unique characteristics of visual technology in terms of data modalities and application scenarios, computer vision poses specific ethical issues. However, the majority of existing literature either addresses artificial intelligence as a whole or pays particular attention to natural language processing, leaving a gap in specialized research on ethical issues and systematic solutions in the field of computer vision. This paper utilizes bibliometrics and text-mining techniques to quantitatively analyze papers from prominent academic conferences in computer vision over the past decade. It first reveals the developing trends and specific distribution of attention regarding trustworthy aspects in the computer vision field, as well as the inherent connections between ethical dimensions and different stages of visual model development. A life-cycle framework regarding trustworthy computer vision is then presented by making the relevant trustworthy issues, the operation pipeline of AI models, and viable technical solutions interconnected, providing researchers and policymakers with references and guidance for achieving trustworthy CV. Finally, it discusses particular motivations for conducting trustworthy practices and underscores the consistency and ambivalence among various trustworthy principles and technical attributes.

Mining companies have long had a questionable reputation for social responsibility, especially in developing countries. In recent years, mining companies operating in developing countries have come under increased pressure as opponents have placed them under greater public scrutiny. Mining companies have responded by developing global corporate social responsibility strategies as part of their larger global business strategies. In these strategies, a prominent place is given to their relationship with local communities. For business ethics, one basic issue is whether such an approach to corporate responsibility is likely to effectively address the development concerns of local communities in developing countries. This paper addresses this question by investigating how the corporate social responsibility agenda of a major minor company has been implemented by one of its subsidiaries in South Africa.

Two court cases that involve selling prescription data for pharmaceutical marketing affect biomedical informatics, patient and clinician privacy, and regulation. Sorrell v. IMS Health Inc. et al. in the United States and R v. Department of Health, Ex Parte Source Informatics Ltd. in the United Kingdom concern privacy and health data protection, data de-identification and reidentification, drug detailing (marketing), commercial benefit from the required disclosure of personal information, clinician privacy and the duty of confidentiality, beneficial and unsavory uses of health data, regulating health technologies, and considering data as speech. Individuals should, at the very least, be aware of how data about them are collected and used. Taking account of how those data are used is needed so societal norms and law evolve ethically as new technologies affect health data privacy and protection.

Environmental impact assessment (EIA) is used globally to manage the impacts of development projects on the environment, so there is an imperative to demonstrate that it can effectively identify risky projects. However, despite the widespread use of quantitative predictive risk models in areas such as toxicology, ecosystem modelling and water quality, the use of predictive risk tools to assess the overall expected environmental impacts of major construction and development proposals is comparatively rare. A risk-based approach has many potential advantages, including improved prediction and attribution of cause and effect; sensitivity analysis; continual learning; and optimal resource allocation. In this paper we investigate the feasibility of using a Bayesian belief network (BBN) to quantify the likelihood and consequence of non-compliance of new projects based on the occurrence probabilities of a set of expert-defined features. The BBN incorporates expert knowledge and continually improves its predictions based on new data as it is collected. We use simulation to explore the trade-off between the number of data points and the prediction accuracy of the BBN, and find that the BBN could predict risk with 90% accuracy using approximately 1000 data points. Although a further pilot test with real project data is required, our results suggest that a BBN is a promising method to monitor overall risks posed by development within an existing EIA process given a modest investment in data collection.

Background Event-based social media monitoring and pathogen whole genome sequencing (WGS) will enhance communicable disease surveillance research and systems. If linked electronically and scanned systematically, the information provided by these technologies could be mined to uncover new epidemiological patterns and associations much faster than traditional public health approaches. The benefits of earlier outbreak detection are significant, but implementation could be opposed in the absence of a social licence or if ethical and legal concerns are not addressed. Methods A three-phase mixed-method Delphi survey with Australian policy-makers, health practitioners and lawyers ( n  = 44) was conducted to explore areas of consensus and disagreement over (1) key policy and practical issues raised by the introduction of novel communicable disease surveillance programmes; and (2) the most significant and likely risks from using social media content and WGS technologies in epidemiological research and outbreak investigations. Results Panellists agreed that the integration of social media monitoring and WGS technologies into communicable disease surveillance systems raised significant issues, including impacts on personal privacy, medicolegal risks and the potential for unintended consequences. Notably, their concerns focused on how these technologies should be used, rather than how the data was collected. Panellists held that social media users should expect their posts to be monitored in the interests of public health, but using those platforms to contact identified individuals was controversial. The conditions of appropriate use of pathogen WGS in epidemiological research and investigations was also contentious. Key differences amongst participants included the necessity for consent before testing and data-linkage, thresholds for action, and the legal and ethical importance of harms to individuals and commercial entities. The erosion of public trust was seen as the most significant risk from the systematic use of these technologies. Conclusions Enhancing communicable disease surveillance with social-media monitoring and pathogen WGS may cause controversy. The challenge is to determine and then codify how these technologies should be used such that the balance between individual risk and community benefit is widely accepted. Participants agreed that clear guidelines for appropriate use that address legal and ethical concerns need to be developed in consultation with relevant experts and the broader Australian public.