Abstract

Abstract
Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many technology products and their fields of application. Machine learning, as a major part of the current AI solutions, can learn from the data and through experience to reach high performance on various tasks. This growing success of AI algorithms has led to a need for interpretability to understand opaque models such as deep neural networks. Various requirements have been raised from different domains, together with numerous tools to debug, justify outcomes, and establish the safety, fairness and reliability of the models. This variety of tasks has led to inconsistencies in the terminology with, for instance, terms such as interpretable, explainable and transparent being often used interchangeably in methodology papers. These words, however, convey different meanings and are weighted differently across domains, for example in the technical and social sciences. In this paper, we propose an overarching terminology of interpretability of AI systems that can be referred to by the technical developers as much as by the social sciences community to pursue clarity and efficiency in the definition of regulations for ethical and reliable AI development. We show how our taxonomy and definition of interpretable AI differ from the ones in previous research and how they apply with high versatility to several domains and use cases, proposing a-highly needed-standard for the communication among interdisciplinary areas of AI.

Abstract
Purpose - Humanity and all life depend on the natural environment of Planet Earth, and that environment is in acute crisis across land, sea and air. One of a set of commentaries on how service can address the UN's sustainable development goals (SDGs), the authors focus on environmental goals SDG 13 (climate action), SDG 14 (life below water) and SDG 15 (life on land). This paper aims to propose a conceptual framework that incorporates the natural environment into transformative services. Design/methodology/approach - The authors trace the evolution of service thinking about the natural environment, from a stewardship perspective of the environment as a set of resources to be managed, through an acknowledgement of nonhuman organisms as actors that can participate in service exchange, towards an emergent concept of ecosystems as integrating human social actors and other biological actors who engage fully in value co-creation. Findings - The authors derive a framework integrating human and other life forms as co-creating actors, drawing on shared natural resources to achieve mutualism, where each actor can have a net benefit from the relationship. Future research questions are posited that may help services research address SDGs 13-15. Originality/value - The framework integrates ideas from environmental ecosystem literature to inform the nature of ecosystems. By integrating environmental actors and ecological insights into the understanding of service ecosystems, service scholars are well placed to make unique contributions to the global challenge of creating a sustainable future.

Abstract
Magnetic hard disk drives continue to be heavily used to store global information. However, due to the physical characteristics these components fatigue and fail, sometimes in unexpected ways. A failing hard disk can cause problems to a group of hard disks and result in suboptimal performance which impacts cloud providers. To address failures, redundancies are put in place, but these redundancies have a high cost. Utilizing Machine learning we identify predictive failure features within a hard disk vendor's Hard Disk Drive Model line which can be used as an early failure prediction method which may be used to reduce redundancies in cloud storage infrastructures.

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Abstract
The Strategic Framework for European Cooperation in Education and Training (ET 2020) aimed to pro-mote the exchange of best practices among the Member States. This paper assesses the performance evo-lution of European countries in terms of the common objectives for the education sector. The framework used to evaluate European education systems is based on constructing a composite indicator adopting a benefit-of-the-doubt approach. The evaluation of performance change over time is done using a Global Malmquist Index. Sigma and beta convergence of EU countries are also explored using non-parametric frontier techniques. The results are analysed for the period 2009-2018 and discussed in light of the goals envisaged and the national policies adopted. The results revealed a trend of improvement in the perfor-mance of education systems in most European countries in the period analysed. Although most European countries moved closer to the European best practice frontier over time, as confirmed by the values of sigma-convergence, a few countries are still lagging considerably below their peers, as revealed by the existence of divergence in beta.(c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )