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    EUROPEAN COMMISSION Brussels, 21.4.2021 COM(2021) 206 final 2021/0106(COD) Proposal for a



    {SEC(2021) 167 final} - {SWD(2021) 84 final} - {SWD(2021) 85 final}





    Reasons for and objectives of the proposal

    This explanatory memorandum accompanies the proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Artificial Intelligence (AI) is a fast evolving family of technologies that can bring a wide array of economic and societal benefits across the entire spectrum of industries and social activities. By improving prediction, optimising operations and resource allocation, and personalising service delivery, the use of artificial intelligence can support socially and environmentally beneficial outcomes and provide key competitive advantages to companies and the European economy. Such action is especially needed in high-impact sectors, including climate change, environment and health, the public sector, finance, mobility, home affairs and agriculture. However, the same elements and techniques that power the socio-economic benefits of AI can also bring about new risks or negative consequences for individuals or the society. In light of the speed of technological change and possible challenges, the EU is committed to strive for a balanced approach. It is in the Union interest to preserve the EU’s technological leadership and to ensure that Europeans can benefit from new technologies developed and functioning according to Union values, fundamental rights and principles.

    This proposal delivers on the political commitment by President von der Leyen, who announced in her political guidelines for the 2019-2024 Commission “A Union that strives for more” 1 , that the Commission would put forward legislation for a coordinated European approach on the human and ethical implications of AI. Following on that announcement, on 19 February 2020 the Commission published the White Paper on AI - A European approach to excellence and trust 2 . The White Paper sets out policy options on how to achieve the twin objective of promoting the uptake of AI and of addressing the risks associated with certain uses of such technology. This proposal aims to implement the second objective for the development of an ecosystem of trust by proposing a legal framework for trustworthy AI. The proposal is based on EU values and fundamental rights and aims to give people and other users the confidence to embrace AI-based solutions, while encouraging businesses to develop them. AI should be a tool for people and be a force for good in society with the ultimate aim of increasing human well-being. Rules for AI available in the Union market or otherwise affecting people in the Union should therefore be human centric, so that people can trust that the technology is used in a way that is safe and compliant with the law, including the respect of fundamental rights. Following the publication of the White Paper, the Commission launched a broad stakeholder consultation, which was met with a great interest by a large number of stakeholders who were largely supportive of regulatory intervention to address the challenges and concerns raised by the increasing use of AI.

    The proposal also responds to explicit requests from the European Parliament (EP) and the European Council, which have repeatedly expressed calls for legislative action to ensure a well-functioning internal market for artificial intelligence systems (‘AI systems’) where both benefits and risks of AI are adequately addressed at Union level. It supports the objective of the Union being a global leader in the development of secure, trustworthy and ethical artificial intelligence as stated by the European Council 3 and ensures the protection of ethical principles as specifically requested by the European Parliament 4 .

    In 2017, the European Council called for a ‘sense of urgency to address emerging trends’ including ‘issues such as artificial intelligence …, while at the same time ensuring a high level of data protection, digital rights and ethical standards’ 5 . In its 2019 Conclusions on the Coordinated Plan on the development and use of artificial intelligence Made in Europe 6 , the Council further highlighted the importance of ensuring that European citizens’ rights are fully respected and called for a review of the existing relevant legislation to make it fit for purpose for the new opportunities and challenges raised by AI. The European Council has also called for a clear determination of the AI applications that should be considered high-risk 7 .

    The most recent Conclusions from 21 October 2020 further called for addressing the opacity, complexity, bias, a certain degree of unpredictability and partially autonomous behaviour of certain AI systems, to ensure their compatibility with fundamental rights and to facilitate the enforcement of legal rules 8 .

    The European Parliament has also undertaken a considerable amount of work in the area of AI. In October 2020, it adopted a number of resolutions related to AI, including on ethics 9 , liability 10 and copyright 11 . In 2021, those were followed by resolutions on AI in criminal matters 12 and in education, culture and the audio-visual sector 13 . The EP Resolution on a Framework of Ethical Aspects of Artificial Intelligence, Robotics and Related Technologies specifically recommends to the Commission to propose legislative action to harness the opportunities and benefits of AI, but also to ensure protection of ethical principles. The resolution includes a text of the legislative proposal for a regulation on ethical principles for the development, deployment and use of AI, robotics and related technologies. In accordance with the political commitment made by President von der Leyen in her Political Guidelines as regards resolutions adopted by the European Parliament under Article 225 TFEU, this proposal takes into account the aforementioned resolution of the European Parliament in full respect of proportionality, subsidiarity and better law making principles.

    Source : eur-lex.europa.eu

    (PDF) Impact of Artificial Intelligence on Firm Performance: Exploring the Mediating Effect of Process

    PDF | Organizations still dependent on information technology innovation have already adopted the in AI subfields and techniques to adapt or disrupt the... | Find, read and cite all the research you need on ResearchGate

    HomeOrganizational ScienceEconBusiness AdministrationDynamic Capabilities

    Conference PaperPDF Available

    Impact of Artificial Intelligence on Firm Performance: Exploring the Mediating Effect of Process-Oriented Dynamic Capabilities

    September 2020


    Conference: The XIII Mediterranean Conference on Information Systems The XVI Conference of the Italian Chapter of AISAt: NAPLESVolume: 38


    Serge-Lopez Wamba-Taguimdje

    Université Côte d'Azur

    Samuel Fosso Wamba

    Toulouse Business School

    Kala Kamdjoug Jean Robert

    Université Cathoque d'Afrique Centrale, Institut Catholique de Yaoundé

    Chris Emmanuel WANKO Tchatchouang

    Université Catholique de l'Afrique Centrale

    Abstract and Figures

    Organizations still dependent on information technology innovation have already adopted the in AI subfields and techniques to adapt or disrupt the market while improvement their performance. Other research has examined the relationship between computing capabilities and organizational performance, with a mediating effect on dynamic process-driven capabilities. We extend this flow of literature and examine the same relationship by taking into account the capabilities of artificial intelligence (AI). Our conceptual framework is based on the paradox of productivity, resource-based view and dynamic capabilities. We relied on an in-depth review of 150 case studies collected on websites related to the integration of AI into organizations. Our study highlights the added value of AI capabilities, in terms of organizational performance, with a focus on improving organizational performance (financial, marketing, and administrative). Our analyses also show that companies improve their performance when they use capabilities of AI to reconfigure their dynamic process-oriented capabilities.

    Conceptual model …

    Sample Item Ratings from the Cases Studies

    … Meditated effects …

    Classification of case studies according to AI technology used

    Figures - uploaded by Kala Kamdjoug Jean RobertAuthor content

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    Source : www.researchgate.net

    Futures of artificial intelligence through technology readiness levels

    Artificial Intelligence (AI) offers the potential to transform our lives in radical ways. However, the main unanswered questions about this foreseen t…

    Telematics and Informatics

    Volume 58, May 2021, 101525

    Futures of artificial intelligence through technology readiness levels

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    Get rights and content

    Under a Creative Commons license

    Open access


    Novel methodology to assess several AI technologies, by mapping them onto TRLs.

    Twelve representative exemplars of AI technologies are assessed, from self-driving cars to virtual assistants.

    Readiness-vs-generality charts resolve the conundrum between readiness and generality and can be used for forecasting.

    Low-generality technologies achieve higher TRLs which are still inaccessible for more general capabilities.

    High-layer generalities may indicate short- or mid-term massive transformative power.


    Artificial Intelligence (AI) offers the potential to transform our lives in radical ways. However, the main unanswered questions about this foreseen transformation are its depth, breadth and timelines. To answer them, not only do we lack the tools to determine what achievements will be attained in the near future, but we even ignore what various technologies in present-day AI are capable of. Many so-called breakthroughs in AI are associated with highly-cited research papers or good performance in some particular benchmarks. However, research breakthroughs do not directly translate into a technology that is ready to use in real-world environments. In this paper, we present a novel exemplar-based methodology to categorise and assess several AI technologies, by mapping them onto Technology Readiness Levels (TRL) (representing their depth in maturity and availability). We first interpret the nine TRLs in the context of AI, and identify several categories in AI to which they can be assigned. We then introduce a generality dimension, which represents increasing layers of breadth of the technology. These two dimensions lead to the new readiness-vs-generality charts, which show that higher TRLs are achievable for low-generality technologies, focusing on narrow or specific abilities, while high TRLs are still out of reach for more general capabilities. We include numerous examples of AI technologies in a variety of fields, and show their readiness-vs-generality charts, serving as exemplars. Finally, we show how the timelines of several AI technology exemplars at different generality layers can help forecast some short-term and mid-term trends for AI.

    Previous articleNext article


    AI technologiesGeneralityCapabilitiesTechnology readinessTRLs

    1. Introduction

    Artificial Intelligence (AI) is poised to have a transformative effect on almost every aspect of our lives, from the viewpoint of individuals, groups, companies and governments. While there are certainly many obstacles to overcome, AI has the potential to empower our daily lives in the immediate future. A great deal of this empowerment comes through the amplification of human abilities. An important space AI systems are also taking over comes from the opportunities of an increasingly more digitised and ‘datafied’ (Hintz et al., 2018) world. Overall, AI is playing an important role in several sectors and applications, from virtual digital assistants in our smartphones to medical diagnosis systems. The impact on the labour market is already very visible, but the workplace may be totally transformed in the following years.

    However, there is already a high degree of uncertainty even when it comes to determining whether a problem can be solved or an occupation can be replaced by AI today (Brynjolfsson et al., 2017, Garcia-Murillo et al., 2018, Martinez-Plumed et al., 2020). The readiness of AI seems to be limited to (1) areas that use and produce a sufficient amount of data and have clear objectives about what the business is trying to achieve; (2) scenarios where the suitable algorithms, approaches and software have been developed to make it fully functional into their relevant fields; and (3) situations whose costs of deployment are affordable. The latter includes some usually neglected dimensions, in addition to performance, such as data, expert knowledge, human oversight, software resources, computing cycles, hardware and network facilities, development time, etc., apart from monetary costs (Martinez-Plumed et al., 2018). To make things more complicated, AI is not one big, specific technology, but it rather consists of several different human-like and non-human-like capabilities, which currently have different levels of development (e.g., from research hypotheses and formulations to more deployed commercial applications). At a high level, AI is composed of reasoning, learning, perception, planning, communication, robotics and social intelligence. At a lower level, there are a myriad applications that combine these abilities with many other components, not necessarily in AI, from driverless cars to chatbots.

    Many products we have today were envisaged decades ago, but have only come into place very recently. For instance, virtual digital assistants, such as Alexa, Siri and Google Home, are still far from some of the imagined possibilities, but they are already successfully answering a wide gamut of requests from customers, and have already become common shoulders to lean on in daily life. Similarly, computers that recognise us have been in our imagination and desiderata for decades, but it is only recently that AI-based face recognition and biometric systems populate smartphones, security cameras and other surveillance equipment for security and safety purposes. Machine learning and other AI techniques are now ubiquitous; recommender systems are used to enhance customers’ experience in retailing and streaming services, fault detection and diagnosis systems are used in industry and healthcare, and planners and optimisers are used in logistics and transportation. Other applications, however, have been announced as imminent, but their deployment in the real world is taking longer than originally expected. For instance, self-driving cars are still taking off very timidly and in very particular contexts.

    Source : www.sciencedirect.com

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