@article {Sidaoui2020, title = {AI feel you: customer experience assessment via chatbot interviews}, journal = {Journal of Service Management}, year = {2020}, abstract = {Purpose: While customer experience (CE) is recognized as a critical determinant of business success, both academics and managers are yet to find a means to gain a comprehensive understanding of CE cost-effectively. The authors argue that the application of relevant AI technology could help address this challenge. Employing interactively prompted narrative storytelling, and the authors investigate the effectiveness of sentiment analysis (SA) on extracting valuable CE insights from primary qualitative data generated via chatbot interviews. Design/methodology/approach: Drawing on a granular and semantically clear framework for studying CE feelings, an artificial intelligence (AI) augmented chatbot was designed. The chatbot interviewed a crowdsourced sample of consumers about their recalled service experience feelings. By combining free-text and closed-ended questions, the authors were able to compare extracted sentiment polarities against established measurement scales and empirically validate our novel approach. Findings: The authors demonstrate that SA can effectively extract CE feelings from primary chatbot data. This findings also suggest that further enhancement in accuracy can be achieved via improvements in the interplay between the chatbot interviewer and SA extraction algorithms. Research limitations/implications: The proposed customer-centric approach can help service companies to study and better understand CE feelings in a cost-effective and scalable manner. The AI-augmented chatbots can also help companies to foster immersive and engaging relationships with customers. This study focuses on feelings, warranting further research on AI{\textquoteright}s value in studying other CE elements. Originality/value: The unique inquisitive role of AI-infused chatbots in conducting interviews and analyzing data in realtime, offers considerable potential for studying CE and other subjective constructs.}, keywords = {artificial intelligence, chatbot, Customer experience, Customer feelings, Sentiment analysis, Storytelling}, issn = {17575818}, doi = {10.1108/JOSM-11-2019-0341}, author = {Sidaoui, Karim and Jaakkola, Matti and Burton, Jamie} } @article {Gentili2020, title = {Are machines stealing our jobs?}, journal = {Cambridge Journal of Regions, Economy and Society}, volume = {13}, number = {1}, year = {2020}, pages = {153{\textendash}173}, abstract = {This study aims to contribute empirical evidence to the debate about the future of work in an increasingly robotised world. We implement a data-driven approach to study the technological transition in six leading Organisation for Economic Co-operation and Development (OECD) countries. First, we perform a cross-country and cross-sector cluster analysis based on the OECD-STAN database. Second, using the International Federation of Robotics database, we bridge these results with those regarding the sectoral density of robots. We show that the process of robotisation is industry- and country-sensitive. In the future, participants in the political and academic debate may be split into optimists and pessimists regarding the future of human labour; however, the two stances may not be contradictory.}, keywords = {cluster analysis, e24, e66, j24, jel classifications, labour dislocation, robotisation}, issn = {1752-1378}, doi = {10.1093/cjres/rsz025}, author = {Gentili, Andrea and Compagnucci, Fabiano and Gallegati, Mauro and Valentini, Enzo} } @conference {Sushina2020, title = {Artificial Intelligence in the Criminal Justice System: Leading Trends and Possibilities}, booktitle = {Proceedings of the 6th International Conference on Social, economic, and academic leadership (ICSEAL-6-2019)}, volume = {441}, year = {2020}, pages = {432{\textendash}437}, publisher = {Atlantis Press}, organization = {Atlantis Press}, address = {Paris, France}, keywords = {artificial intelligence, criminal justice system, digital technologies, Leadership}, isbn = {978-94-6252-974-8}, doi = {10.2991/assehr.k.200526.062}, url = {https://www.atlantis-press.com/article/125940991}, author = {Sushina, Tatyana and Sobenin, Andrew} } @article {Acemoglu2020, title = {Competing with Robots: Firm-Level Evidence from France}, journal = {AEA Papers and Proceedings}, volume = {110}, year = {2020}, pages = {383{\textendash}388}, abstract = {We study the firm-level implications of robot adoption in France. Of 55,390 firms in our sample, 598 adopted robots between 2010 and 2015, but these firms accounted for 20 percent of manufacturing employment. Adopters experienced significant declines in labor shares, the share of production workers in employment, and increases in value added and productivity. They expand their overall employment as well. However, this expansion comes at the expense of competitors, leading to an overall negative association between adoption and employment. Robot adoption has a large impact on the labor share because adopters are larger and grow faster than their competitors.}, keywords = {automation, competition, j23, j24, jel codes, l11, labor share, manufacturing, productivity, reallocation, robots, tasks}, issn = {2574-0768}, doi = {10.1257/pandp.20201003}, author = {Acemoglu, Daron and Lelarge, Claire and Restrepo, Pascual} } @article {Zavrsnik2020, title = {Criminal justice, artificial intelligence systems, and human rights}, journal = {ERA Forum}, volume = {20}, number = {4}, year = {2020}, pages = {567{\textendash}583}, publisher = {The Author(s)}, abstract = {The automation brought about by big data analytics, machine learning and artificial intelligence systems challenges us to reconsider fundamental questions of criminal justice. The article outlines the automation which has taken place in the criminal justice domain and answers the question of what is being automated and who is being replaced thereby. It then analyses encounters between artificial intelligence systems and the law, by considering case law and by analysing some of the human rights affected. The article concludes by offering some thoughts on proposed solutions for remedying the risks posed by artificial intelligence systems in the criminal justice domain.}, keywords = {Algorithms, artificial intelligence, automation, Criminal justice, Fair trial, Human rights}, isbn = {1202702000602}, issn = {18639038}, doi = {10.1007/s12027-020-00602-0}, url = {http://dx.doi.org/10.1007/s12027-020-00602-0}, author = {Zavr{\v s}nik, Ale{\v s}} } @article {GomezdeAgreda2020, title = {Ethics of autonomous weapons systems and its applicability to any AI systems}, journal = {Telecommunications Policy}, volume = {5}, number = {5}, year = {2020}, pages = {101953}, publisher = {Elsevier Ltd}, abstract = {Most artificial intelligence technologies are dual-use. They are incorporated into both peaceful civilian applications and military weapons systems. Most of the existing codes of conduct and ethical principles on artificial intelligence address the former while largely ignoring the latter. But when these technologies are used to power systems specifically designed to cause harm, the question must be asked as to whether the ethics applied to military autonomous systems should also be taken into account for all artificial intelligence technologies susceptible of being used for those purposes. However, while a freeze in investigations is neither possible nor desirable, neither is the maintenance of the current status quo. Comparison between general-purpose ethical codes and military ones concludes that most ethical principles apply to human use of artificial intelligence systems as long as two characteristics are met: that the way algorithms work is understood and that humans retain enough control. In this way, human agency is fully preserved and moral responsibility is retained independently of the potential dual-use of artificial intelligence technology.}, keywords = {AI ethics, Autonomous weapons, CCW, Dual-use AI, Explainability, Meaningful human control}, issn = {03085961}, doi = {10.1016/j.telpol.2020.101953}, url = {https://doi.org/10.1016/j.telpol.2020.101953}, author = {G{\'o}mez de {\'A}greda, {\'A}ngel} } @article {Hacker2020, title = {Explainable AI under contract and tort law: legal incentives and technical challenges}, journal = {Artificial Intelligence and Law}, number = {0123456789}, year = {2020}, publisher = {Springer Netherlands}, abstract = {This paper shows that the law, in subtle ways, may set hitherto unrecognized incentives for the adoption of explainable machine learning applications. In doing so, we make two novel contributions. First, on the legal side, we show that to avoid liability, professional actors, such as doctors and managers, may soon be legally compelled to use explainable ML models. We argue that the importance of explainability reaches far beyond data protection law, and crucially influences questions of contractual and tort liability for the use of ML models. To this effect, we conduct two legal case studies, in medical and corporate merger applications of ML. As a second contribution, we discuss the (legally required) trade-off between accuracy and explainability and demonstrate the effect in a technical case study in the context of spam classification.}, keywords = {Contract law, Corporate takeovers, Explainability, Explainability-accuracy trade-off, Explainable AI, Interpretable machine learning, Medical malpractice, Tort law}, isbn = {0123456789}, issn = {15728382}, doi = {10.1007/s10506-020-09260-6}, url = {https://doi.org/10.1007/s10506-020-09260-6}, author = {Hacker, Philipp and Krestel, Ralf and Grundmann, Stefan and Naumann, Felix} } @article {Reid-musson2020, title = {Feminist economic geography and the future of work}, journal = {EPA: Economy and Space}, year = {2020}, pages = {1{\textendash}12}, keywords = {corresponding author, department of geography, emily reid-musson, feminist economic geography, memorial university of newfoundland, newfoundland and labrador a1c, social reproduction, subjectivity, technology, work}, doi = {10.1177/0308518X20947101}, author = {Reid-musson, Emily and Cockayne, Daniel and Frederiksen, Lia and Worth, Nancy} } @booklet {Suran2020, title = {Frameworks for collective intelligence: A systematic literature review}, howpublished = {ACM Computing Surveys}, volume = {53}, number = {1}, year = {2020}, pages = {1{\textendash}36}, abstract = {Over the last few years, Collective Intelligence (CI) platforms have become a vital resource for learning, problem solving, decision-making, and predictions. This rising interest in the topic has to led to the development of several models and frameworks available in published literature. Unfortunately, most of these models are built around domain-specific requirements, i.e., they are often based on the intuitions of their domain experts and developers. This has created a gap in our knowledge in the theoretical foundations of CI systems and models, in general. In this article, we attempt to fill this gap by conducting a systematic review of CI models and frameworks, identified from a collection of 9,418 scholarly articles published since 2000. Eventually, we contribute by aggregating the available knowledge from 12 CI models into one novel framework and present a generic model that describes CI systems irrespective of their domains. We add to the previously available CI models by providing a more granular view of how different components of CI systems interact. We evaluate the proposed model by examining it with respect to six popular, ongoing CI initiatives available on the Web.}, keywords = {collective intelligence, Crowdsourcing, Human computer interaction, Systematic literature review, Web 2.0, Wisdom of crowds}, issn = {15577341}, doi = {10.1145/3368986}, author = {Suran, Shweta and Pattanaik, Vishwajeet and Draheim, Dirk} } @article {EganadelSol2020, title = {The Future of Work in Developing Economies}, journal = {MIT Sloan Management Review}, volume = {61}, number = {2}, year = {2020}, pages = {1{\textendash}3}, abstract = {

Much has been written about the rise of automation in developed countries. Economists have been busily creating models seeking to quantify the likely impact of automation on employment. However, far less has been written about the potential effects on work in developing nations. This is surprising, given that automation may be especially troublesome for developing economies. Here, del Sol and Joyce examine the effects of large-scale automation on workers in developing countries.

}, keywords = {Armenia, Asia, Austria, automation, Bolivia, Business And Economics{\textendash}Management, China, Developing countries{\textendash}LDCs, Employment, future, Georgia (country), Ghana, Impact analysis, Kenya, Kuala Lumpur Malaysia, Laos, Republic of North Macedonia, South Korea, Sri Lanka, United States{\textendash}US, Vietnam, Workers}, issn = {15329194}, author = {Egana del Sol, Pablo and Joyce, Connor and Del Sol, Pablo Ega{\~n}a and Joyce, Connor} } @proceedings {9999, title = {Impacts of the Use of Machine Learning on Work Design}, year = {2020}, month = {11/2020}, publisher = {ACM}, address = {Virtual Event, NSW, Australia}, abstract = {

The increased pervasiveness of technological advancements in automation makes it urgent to address the question of how work is changing in response. Focusing on applications of machine learning (ML) to automate information tasks, we draw on a simple framework for identifying the impacts of an automated system on a task that suggests 3 patterns for the use of ML{\textemdash}decision support, blended decision making and complete automation. In this paper, we extend this framework by considering how automation of one task might have implications for interdependent tasks and how automation applies to coordination mechanisms.

}, keywords = {artificial intelligence, automation, Coordination, machine learning, work design}, isbn = {978-1-4503-8054-6/20/11}, doi = {10.1145/3406499.3415070}, attachments = {https://waim.network/sites/crowston.syr.edu/files/Impacts_of_ML_for_HAI_2020.pdf}, author = {Kevin Crowston and Bolici, Francesco} } @article {Jung2020, title = {Industrial robots, employment growth, and labor cost: A simultaneous equation analysis}, journal = {Technological Forecasting and Social Change}, volume = {159}, number = {June}, year = {2020}, pages = {120202}, publisher = {Elsevier}, abstract = {In recent years, the global rapid expansion of industrial robots has induced ever-increasing concerns for the cause and effect of such growth, particularly with regard to its relationship with labor. This paper analyzes the factors underlying the adoption of industrial robots, employment growth and structure, and labor costs, taking into account the two-way causalities between these variables. For the empirical analysis, we use the three-stage least squares (3SLS) method for the system of simultaneous equations and apply it to the panel data constructed for 42 countries. Explanatory variables for each equation include the dependent variables of other equations and exogenous variables, such as the labor market environment, physical and human capital, and country-specific social environment. The empirical results of the present study indicate that the increase in both unit labor costs and hourly compensation level induces an extensive application of industrial robots. Subsequently, the expansion of industrial robots leads to a reduction of unit labor costs; however, the hourly compensation level increases, implying that the productivity-enhancing effect exceeds the wage-increasing effect of industrial robots. The extensive use of industrial robots tends to suppress employment growth, confirming the labor-substituting effect of industrial robots; the observed trend disproportionately affects low-skilled labor.}, keywords = {Compensation level, Employment growth, Industrial robots, Labor cost, Simultaneous equation analysis}, issn = {00401625}, doi = {10.1016/j.techfore.2020.120202}, url = {https://doi.org/10.1016/j.techfore.2020.120202}, author = {Jung, Jin Hwa and Lim, Dong Geon} } @article {Kernbach2020, title = {Machine learning-based clinical prediction modeling {\textendash} A practical guide for clinicians}, journal = {Artificial Intelligence in Precision Health}, number = {June}, year = {2020}, pages = {257{\textendash}278}, publisher = {Elsevier Inc.}, keywords = {Alzheimers disease detection, artificial intelligence, Convolutional neural networks, Deep neural networks, Ensemble machine learning methods}, isbn = {9780128171332}, doi = {10.1016/b978-0-12-817133-2.00011-2}, url = {https://arxiv.org/abs/2006.15069v1 http://dx.doi.org/10.1016/B978-0-12-817133-2.00011-2}, author = {Kernbach, Julius M. and Staartjes, Victor E.} } @booklet {Willcocks2020, title = {Robo-Apocalypse cancelled? Reframing the automation and future of work debate}, howpublished = {Journal of Information Technology}, number = {2016}, year = {2020}, abstract = {Robotics and the automation of knowledge work, often referred to as AI (artificial intelligence), are presented in the media as likely to have massive impacts, for better or worse, on jobs skills, organizations and society. The article deconstructs the dominant hype-and-fear narrative. Claims on net job loss emerge as exaggerated, but there will be considerable skills disruption and change in the major global economies over the next 12 years. The term AI has been hijacked, in order to suggest much more going on technologically than can be the case. The article reviews critically the research evidence so far, including the author{\textquoteright}s own, pointing to eight major qualifiers to the dominant discourse of major net job loss from a seamless, overwhelming AI wave sweeping fast through the major economies. The article questions many assumptions: that automation creates few jobs short or long term; that whole jobs can be automated; that the technology is perfectible; that organizations can seamlessly and quickly deploy AI; that humans are machines that can be replicated; and that it is politically, socially and economically feasible to apply these technologies. A major omission in all studies is factoring in dramatic increases in the amount of work to be done. Adding in ageing populations, productivity gaps and skills shortages predicted across many G20 countries, the danger might be too little, rather than too much labour. The article concludes that, if there is going to be a Robo-Apocalypse, this will be from a collective failure to adjust to skills change over the next 12 years. But the debate needs to be widened to the impact of eight other technologies that AI insufficiently represents in the popular imagination and that, in combination, could cause a techno-apocalypse.}, keywords = {AI, automation, cognitive automation, future of work, Information Technology, Jobs, robotic process automation, skills}, isbn = {0268396220925}, issn = {14664437}, doi = {10.1177/0268396220925830}, author = {Willcocks, Leslie} } @article {Arduengo2020, title = {The Robot Economy : Here It Comes}, journal = {International Journal of Social Robotics}, number = {July}, year = {2020}, publisher = {Springer Netherlands}, keywords = {blockchain, Cloud robotics, Intelligent robots, IoRT, Robot economy}, issn = {1875-4805}, doi = {10.1007/s12369-020-00686-1}, url = {https://doi.org/10.1007/s12369-020-00686-1}, author = {Arduengo, Miguel and Sentis, Luis} } @article {Petersen2020, title = {The Role of Discretion in the Age of Automation}, journal = {Computer Supported Cooperative Work: CSCW: An International Journal}, volume = {29}, number = {3}, year = {2020}, pages = {303{\textendash}333}, abstract = {This paper examines the nature of discretion in social work in order to debunk myths dominating prevalent debates on digitisation and automation in the public sector. Social workers have traditionally used their discretion widely and with great autonomy, but discretion has increasingly come under pressure for its apparent subjectivity and randomness. In Denmark, our case in point, the government recently planned to standardise laws to limit or remove discretion where possible in order for automation of case management to gain a foothold. Recent studies have focused on discretion in the public sector, but few have examined it explicitly and as part of real cases. As a consequence, they often leave the myths about discretion unchallenged. Inspired by the literature on discretion and CSCW research on rules in action, this study reports on an empirical investigation of discretion in child protection services in Denmark. The results of our analysis provide a new understanding of discretion as a cooperative endeavour, based on consultation and skill, rather than an arbitrary or idiosyncratic choice. In this manner, our study contradicts the myth of discretion inherent in the automation agenda. Correspondingly, we ask for attention to be given to systems that integrate discretion with technology rather than seek to undermine it directly or get around it surreptitiously. In this age of automation, this is not only an important but also an urgent task for CSCW researchers to fulfil.}, keywords = {Administrative work, automation, Casework, Decision-Making, Digital-ready legislation, Digitisation, Discretion, Rules in action, Social work}, issn = {15737551}, doi = {10.1007/s10606-020-09371-3}, author = {Petersen, Anette C.M. and Christensen, Lars Rune and Hildebrandt, Thomas T.} } @article {Theresa2020, title = {Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation}, journal = {The TQM Journal}, volume = {ahead-of-p}, number = {ahead-of-print}, year = {2020}, month = {jan}, abstract = {Purpose This paper explores if and how Artificial Intelligence can contribute to marketing strategy formulation.Design/methodology/approach Qualitative research based on exploratory in-depth interviews with industry experts currently working with artificial intelligence tools.Findings Key themes include: (1) Importance of AI in strategic marketing decision management; (2) Presence of AI in strategic decision management; (3) Role of AI in strategic decision management; (4) Importance of business culture for the use of AI; (5) Impact of AI on the business{\textquoteright} organizational model. A key consideration is a {\textquotedblleft}creative-possibility perspective,{\textquotedblright} highlighting the future potential to use AI not only for rational but also for creative thinking purposes.Research limitations/implications This work is focused only on strategy creation as a deliberate process. For this, AI can be used as an effective response to the external contingencies of high volumes of data and uncertain environmental conditions, as well as being an effective response to the external contingencies of limited managerial cognition. A key future consideration is a {\textquotedblleft}creative-possibility perspective.{\textquotedblright}Practical implications A practical extension of the Gartner Analytics Ascendancy Model (Maoz, 2013).Originality/value This paper aims to contribute knowledge relating to the role of AI in marketing strategy formulation and explores the potential avenues for future use of AI in the strategic marketing process. This is explored through the lens of contingency theory, and additionally, findings are expressed using the Gartner analytics ascendancy model.}, keywords = {AI, artificial intelligence, creativity, marketing strategy, marketing synergy, paper type research paper, rationality, tqm}, isbn = {1754-2731}, doi = {10.1108/TQM-12-2019-0303}, url = {https://doi.org/10.1108/TQM-12-2019-0303}, author = {Theresa, Eriksson and Alessandro, Bigi and Michelle, Bonera and Eriksson, Theresa and Bigi, Alessandro and Bonera, Michelle and Theresa, Eriksson and Alessandro, Bigi and Michelle, Bonera} } @article {2019, title = {The future of the work in America}, year = {2019}, month = {07/2019}, keywords = {consulting reports}, url = {https://www.mckinsey.com/featured-insights/future-of-work/the-future-of-work-in-america-people-and-places-today-and-tomorrow}, author = {Susan Lund and James Manyika and Liz Hilton Segal and Andre Dua and Bryan Hancock and Scott Rutherford and Brent Macon} } @article {2019, title = {The future of women at work}, volume = {McKinsey Global Institute}, year = {2019}, month = {06/2019}, keywords = {consulting reports}, url = {https://www.mckinsey.com/~/media/mckinsey/featured\%20insights/gender\%20equality/the\%20future\%20of\%20women\%20at\%20work\%20transitions\%20in\%20the\%20age\%20of\%20automation/mgi-the-future-of-women-at-work-full-report-june\%202019.ashx}, author = {Anu Madgavkar and James Manyika and Mekala Krishnan and Kweilin Ellingrud and Lareina Yee and Jonathan Woetzel and Michael Chui and Vivian Hunt and Sruti Balakrishnan} } @article {2019, title = {Ten ways the precautionary principle undermines progress in artificial intelligence}, year = {2019}, month = {02/2019}, keywords = {consulting reports}, url = {https://itif.org/publications/2019/02/04/ten-ways-precautionary-principle-undermines-progress-artificial-intelligence}, author = {Daniel Castro and Michael McLaughlin} } @article {2018, title = {Automated Classification of chest X-ray images as normal or abnormal using Convolutional Neural Network}, journal = {Asian Journal of Convergence in Technology}, volume = {4}, year = {2018}, month = {04/2018}, abstract = {Chest X-Rays are generally used for diagnosing abnormalities in the thoracic area. Radiologists need to spend significant amount of time for interpreting scans. Automatic classification of these images could greatly help radiology interpretation process by enhancing real world diagnosis of problems. Hence, radiologists can focus on detecting abnormalities from the abnormal images rather than checking for it in all the images. In this paper, we present a machine learning approach to solve this problem. Here, the algorithm uses COnvolutional Neural Networks (CNN) to learn and classify chest X-ray images as normal or abnormal based on image features.}, keywords = {classification, machine learning, radiology}, author = {Aayushi Gupta and Anupama C and P Indumathi and Anuj Kumar} } @article {2018, title = {The discourse approach to boundary identification and corpus construction for theory review articles}, journal = {Journal of the Association for Information Systems}, year = {2018}, keywords = {article identification, boundary identification, citation search, keyword search, literature review, machine learning, research review, review article}, url = {https://www.researchgate.net/publication/325215971_Understanding_the_Elephant_The_Discourse_Approach_to_Boundary_Identification_and_Corpus_Construction_for_Theory_Review_Articles}, author = {Kai R. Larsen and Dirik S. Hovorka and Alan R. Dennis and Jevin D. West} } @article {2018, title = {The evolving role of ICT in the economy}, year = {2018}, month = {06/2018}, keywords = {consulting reports}, url = {http://www.lse.ac.uk/business-and-consultancy/consulting/consulting-reports/the-evolving-role-of-ict-in-the-economy}, author = {Mirko Draca and Ralf Martin and Rosa Sanchis-Guarner} } @article {2018, title = {How real is the impact of artificial intelligence? The business information survey 2018}, journal = {Business Information Review}, volume = {35}, year = {2018}, month = {12/2019}, pages = {99 - 115}, keywords = {Artificial Intelligence (AI), blockchain, chatbot, cybersecurity, data economy, data governance, data lakes, data literacy, data quality, data trusts, data value, ethics, information literacy, intelligent virtual agents, machine learning (ML), Robotics}, issn = {0266-3821}, doi = {10.1177/0266382118790150}, url = {http://journals.sagepub.com/doi/10.1177/0266382118790150}, author = {Carter, Denise} } @article {2018, title = {Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR)}, journal = {Systematic Reviews}, year = {2018}, month = {2018}, publisher = {Springer}, type = {Review}, abstract = {Systematic reviews (SR) are vital to health care, but have become complicated and time-consuming, due to the rapid expansion of evidence to be synthesised. Fortunately, many tasks of systematic reviews have the potential to be automated or may be assisted by automation. Recent advances in natural language processing, text mining and machine learning have produced new algorithms that can accurately mimic human endeavour in systematic review activity, faster and more cheaply. Automation tools need to be able to work together, to exchange data and results. Therefore, we initiated the International Collaboration for the Automation of Systematic Reviews (ICASR), to successfully put all the parts of automation of systematic review production together. The first meeting was held in Vienna in October 2015. We established a set of principles to enable tools to be developed and integrated into toolkits. This paper sets out the principles devised at that meeting, which cover the need for improvement in efficiency of SR tasks, automation across the spectrum of SR tasks, continuous improvement, adherence to high quality standards, flexibility of use and combining components, the need for a collaboration and varied skills, the desire for open source, shared code and evaluation, and a requirement for replicability through rigorous and open evaluation. Automation has a great potential to improve the speed of systematic reviews. Considerable work is already being done on many of the steps involved in a review. The {\textquoteright}Vienna Principles{\textquoteright} set out in this paper aim to guide a more coordinated effort which will allow the integration of work by separate teams and build on the experience, code and evaluations done by the many teams working across the globe.}, keywords = {automation, Collaboration, Systematic review}, doi = {10.1186/s13643-018-0740-7}, author = {Elaine Beller and Justin Clark and Guy Tsafnat and Clive Adams and Heinz Diehl and Hans Lund and Mourad Ouzzani and Kristina Thayer and James Thomas and Tari Turner and Jun Xia and Karen Robinson and Paul Glasziou} } @article {2018, title = {Robot vs. tax inspector or how the fourth industrial revolution will change the tax system: a review of problems and solutions}, journal = {Journal of Tax Reform}, volume = {4}, year = {2018}, month = {Jan-01-2018}, pages = {6 - 26}, abstract = {The fourth Industiral Revolution and the accelerated development of cyber-physical technologies lead to essential changes in national tax systems and international taxation. The main areas in which taxation meets cyber-physical technologies are digitization, robotization, M2M and blockchain technologies. Each of these areas has its own opportunities and problems. Three main approaches towards possible solutions for these new problems are identified. The first is to try to apply taxation to new cyber-physical technologies and products of their application. This approach includes the OECD{\textquoteright}s Action 1 plan on Base Erosion and Profit Shifting. It also includes the spread of traditional taxes on new objects - personal data, cryptocurrencies, imputed income of robots. The second is to replace digital transactions and shortfalls in revenues by traditional objects of taxation in the form of tangible assets and people and / or increase tax pressure (including by improving tax administration with use of Big Data) and the degree of progressiveness of taxes already levied on such objects. The third approach is to set a course on building a new tax space with smart taxes based on real-time principles, smart contracts and Big Data. This implies a transition to automatic taxation using blockchain technologies, which focus on the functions of applying distributed ledgers of business transactions in real-time. At present, the general trends are such that the first and second are prevalent, which is manifested in an increase in the relative importance of property, sales and employment taxes. Concerning the third approach, any movement in this direction is still facing a number oftechnical and other problems and is thus being discussed mainly at the conceptual level}, keywords = {blockchain, cyber-physical technologies, digitization, taxes in Big Data, taxes on cryptocurrencies, taxes on digital goods, taxes on robots}, issn = {24128872}, doi = {10.15826/jtr.2018.4.1.042}, url = {https://jtr.urfu.ru/en/archive/journal/95/article/1113/}, author = {Vishnevsky, Valentine P. and Chekina, Viktoriia D.} } @article {2017, title = {The future of skills employment in 2030}, year = {2017}, keywords = {consulting reports}, url = {https://futureskills.pearson.com/research/assets/pdfs/technical-report.pdf} } @article {2017, title = {Jobs lost, jobs gained: Workforce transactions in a time of automation}, year = {2017}, keywords = {consulting reports}, url = {https://www.mckinsey.com/~/media/mckinsey/featured\%20insights/future\%20of\%20organizations/what\%20the\%20future\%20of\%20work\%20will\%20mean\%20for\%20jobs\%20skills\%20and\%20wages/mgi-jobs-lost-jobs-gained-report-december-6-2017.ashx}, author = {James Manyika and Susan Lund and Michael Chui and Jonathan Woetzel and Ryan Ko and Saurabh Sanghvi and Parul Batra and Jacques Bughin} } @conference {Malone:2017:PPB:2998181.2998343, title = {Putting the Pieces Back Together Again: Contest Webs for Large-Scale Problem Solving}, booktitle = {Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing}, year = {2017}, pages = {1661{\textendash}1674}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, keywords = {climate change, collective intelligence, contest webs, contests, Coordination, incentives, knowledge reuse}, isbn = {978-1-4503-4335-0}, doi = {10.1145/2998181.2998343}, url = {http://doi.acm.org/10.1145/2998181.2998343}, author = {Malone, Thomas W. and Jeffrey V Nickerson and Laubacher, Robert J. and Fisher, Laur Hesse and de Boer, Patrick and Han, Yue and Towne, W. Ben} } @article {2015, title = {Artificial intelligence for the real world: Don{\textquoteright}t start with moon shots}, year = {2015}, keywords = {consulting reports}, url = {https://hbr.org/2018/01/artificial-intelligence-for-the-real-world}, author = {Thomas H. Davenport and Rajeev Ronanki} } @article {1994, title = {Design concepts of computer-aided integrated manufacturing systems: Work-psychological concepts and empirical findings}, journal = {International Journal of Industrial Ergonomics}, volume = {17}, year = {1994}, publisher = {Elsevier}, chapter = {11-19}, abstract = {The research project "GRIPS" is investigating the design of computer-aided integrated manufacturing systems from a work psychological perspective. The goal is to develop and empirically support adequate design concepts. The project consists of three phases. Evidence from a broad questionnaire survey indicates that most CIM implementations fail to meet expectations associated therewith. Based on the assumption that only the joint optimization of social and technical system results in humane working conditions and economic efficiency, implementations and use of CIM systems has been investigated in 60 companies in Switzerland. THe conceptual framework distinguishes technically-oriented and work-oriented design concepts on four levels; the enterprise, the organizational unit, the group and the individual. Work-oriented manufacturing systems - as opposed to technically-oriented ones - are characterized by decentralization, functional integration, work in self-regulated groups and complete and challenging tasks. The findings support the hypothesis that work-oriented design concepts are related to higher efficiency and better achievement of goals and pursued with the use of new technologies. In the third phase 12 companies have been selected for detailed case studies: The companies are comparable concerning product range and manufacturing conditions but different on the level of work-orientation.}, keywords = {Computer-Integrated-Manufacturing CIM, Organizational design, Production design concepts, Socio-technical system approach, Work psychology, Work-orientation}, author = {C. Kirsh and O. Strohm and E. Ulich} }