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The first article in this series examined why the world wants controls over Artificial Intelligence (AI). This second article discusses how an organisation can manage AI responsibly, in order to protect its own interests, but also...
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The first article in this series examined why the world wants controls over Artificial Intelligence (AI). This second article discusses how an organisation can manage AI responsibly, in order to protect its own interests, but also those of its stakeholders and society as a whole. A limited amount of guidance is provided by ethical analysis. A much more effective approach is to apply adapted forms of the established techniques of risk assessment and risk management. Critically, risk assessment needs to be undertaken not only with the organisation's own interests in focus, but also from the perspectives of other stakeholders. To underpin this new form of business process, a set of Principles for Responsible AI is presented, consolidating proposals put forward by a diverse collection of 30 organisations. (C) 2019 Roger Clarke. Published by Elsevier Ltd. All rights reserved.
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WHO has recommended HPV testing for cervical screening where it is practical and affordable. If used, it is important to both clarify and implement the clinical management of positive results. We estimated the performance in Lusak...
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WHO has recommended HPV testing for cervical screening where it is practical and affordable. If used, it is important to both clarify and implement the clinical management of positive results. We estimated the performance in Lusaka, Zambia of a novel screening/triage approach combining HPV typing with visual assessment assisted by a deep-learning approach called automated visual evaluation (AVE).In this well-established cervical cancer screening program nested inside public sector primary care health facilities, experienced nurses examined women with high-quality digital cameras; the magnified illuminated images permit inspection of the surface morphology of the cervix and expert telemedicine quality assurance. Emphasizing sensitive criteria to avoid missing precancer/cancer, ~ 25% of women screen positive, reflecting partly the high HIV prevalence. Visual screen-positive women are treated in the same visit by trained nurses using either ablation (~ 60%) or LLETZ excision, or referred for LLETZ or more extensive surgery as needed. We added research elements (which did not influence clinical care) including collection of HPV specimens for testing and typing with BD OnclarityTM with a five channel output (HPV16, HPV18/45, HPV31/33/52/58, HPV35/39/51/56/59/66/68, human DNA control), and collection of triplicate cervical images with a Samsung Galaxy J8 smartphone cameraTM that were analyzed using AVE, an AI-based algorithm pre-trained on a large NCI cervical image archive. The four HPV groups and three AVE classes were crossed to create a 12-level risk scale, ranking participants in order of predicted risk of precancer. We evaluated the risk scale and assessed how well it predicted the observed diagnosis of precancer/cancer.HPV type, AVE classification, and the 12-level risk scale all were strongly associated with degree of histologic outcome. The AVE classification showed good reproducibility between replicates, and added finer predictive accuracy to each HPV type group. Women living with HIV had higher prevalence of precancer/cancer; the HPV-AVE risk categories strongly predicted diagnostic findings in these women as well.These results support the theoretical efficacy of HPV-AVE-based risk estimation for cervical screening. If HPV testing can be made affordable, cost-effective and point of care, this risk-based approach could be one management option for HPV-positive women.
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Incorporating ethics and values within the life cycle of an AI asset means to secure, under these perspectives, its development, deployment, use and decommission. These processes must be done safely, following current legislation,...
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Incorporating ethics and values within the life cycle of an AI asset means to secure, under these perspectives, its development, deployment, use and decommission. These processes must be done safely, following current legislation, and incorporating the social needs towards having greater well-being over the agents and environment involved. Standards, frameworks and ethical imperatives—which are also considered a backbone structure for legal considerations—drive the development process of new AI assets for industry. However, given the lack of concrete standards and robust AI legislation, the gap between ethical principles and actionable approaches is still considerable. Different organisations have developed various methods based on multiple ethical principles to facilitate practitioners developing AI components worldwide. Nevertheless, these approaches can be driven by a self-claimed ethical shell or without a clear understanding of the impacts and risks involved in using their AI assets. The manufacturing sector has produced standards since 1990’s to guarantee, among others, the correct use of mechanical machinery, workers security, and environmental impact. However, a revision is needed to blend these with the needs associated with AI’s use. We propose using a vertical-domain framework for the manufacturing sector that will consider ethical perspectives, values, requirements, and well-known approaches related to risk management in the sector.
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This paper conceptualizes how artificial intelligence (AI) may impact the way companies innovate and manage their innovation process. A research framework we use in investigation builds upon three pillars - data, new tech, and tal...
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This paper conceptualizes how artificial intelligence (AI) may impact the way companies innovate and manage their innovation process. A research framework we use in investigation builds upon three pillars - data, new tech, and talent. Based on it, we map and discuss changes for organizations applying AI in innovation management. We conceptualize innovation management in the era of AI as a data-driven process in which AI significantly affects all dimensions of the innovation process and its management. Further, our framework suggests that the need for data, technology, and talents will lead to more open and collaborative innovation approaches, novel strategies for innovation protection, and the emergence of new roles in innovation teams. Using AI for innovation management also creates challenges like ethical data usage, navigation through diversity emerging from humans collaborating with artificial intelligence, and escaping from the incremental innovation trap. We summarize our main conclusions as research propositions and outline their practical implications.
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Open Forum & Panel Discussion on the Use of the IALA Risk Management Toolboxが、2018年10月4 日、東京にてInternational Association of Marine Aids to Navigation and Lighthouse Authorities (IALA)主催のもと、一般財団法人日本航路標識協会の運営で開催された。...
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Open Forum & Panel Discussion on the Use of the IALA Risk Management Toolboxが、2018年10月4 日、東京にてInternational Association of Marine Aids to Navigation and Lighthouse Authorities (IALA)主催のもと、一般財団法人日本航路標識協会の運営で開催された。
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The purpose of this paper is to explore the field of Risk Management (RM) in relation with Knowledge Management (KM). It attempts to present a conceptual framework, called Knowledge-Based Risk Management (KBRM) that employs KM pro...
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The purpose of this paper is to explore the field of Risk Management (RM) in relation with Knowledge Management (KM). It attempts to present a conceptual framework, called Knowledge-Based Risk Management (KBRM) that employs KM processes to improve its effectiveness and increase the probability of success in innovative Information Technology (IT) projects. It addresses initiatives towards employing KM processes in RM processes by reviewing, interpreting the related and relevant literature and sheds light on integration with RM in the IT project. The paper exposes some pertinent elements needed for building the KBRM framework for IT projects and also suggests some instrument about the integration of KM and RM process to improve the RRP (Risk Response Planning) process efficiency. This paper will contribute to the literature and practice by providing a clear method for employing KBRM as a framework to keep organizations competitive within the business environment.
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We present the IBM Research Scenario Planning Advisor (SPA), a decision support system that allows users to generate diverse alternate scenarios of the future and enhance their ability to imagine the different possible outcomes, i...
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We present the IBM Research Scenario Planning Advisor (SPA), a decision support system that allows users to generate diverse alternate scenarios of the future and enhance their ability to imagine the different possible outcomes, including unlikely but potentially impactful futures. Our system, takes as input the relevant information from news and social media, representing key risk drivers, as well as the domain knowledge and generates scenarios that explain the key risk drivers and describe the alternative futures. To this end, we provide a characterization of the problem, knowledge engineering methodology, and transformation to AI planning. Furthermore, we describe the computation of the scenarios, lessons learned, and the feedback received from the pilot deployment of the SPA system in IBM.
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Abstract Cloud computing, due to its many advantages like pay per use, elasticity of use, scalability, resource sharing etc. has led the companies already using on premise IT systems to adopt cloud. But as with any other technolog...
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Abstract Cloud computing, due to its many advantages like pay per use, elasticity of use, scalability, resource sharing etc. has led the companies already using on premise IT systems to adopt cloud. But as with any other technology, cloud computing has its own share of risks and issues which can lead to disastrous situation without proper risk aware and risk managed cloud adoption. Also as adopting cloud results in outsourcing data and processes to an agency called as cloud service provider selection of proper service provider, clear contractual statement called Service Level Agreement, cloud awareness becomes most important. The authors propose a framework for managing risk in cloud adoption. The framework uses Fuzzy Inference System for service provider ranking.
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Artificial intelligence (AI) applications have been gaining traction across the radiology space, promising to redefine its workflow and delivery. However, they enter into an uncertain legal environment. This piece examines the nat...
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Artificial intelligence (AI) applications have been gaining traction across the radiology space, promising to redefine its workflow and delivery. However, they enter into an uncertain legal environment. This piece examines the nature, exposure, and theories of liability relevant to musculoskeletal radiologist practice. More specifically, it explores the negligence, vicarious liability, and product liability frameworks by way of illustrative vignettes.
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