%0 Conference Paper %B AIES 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society %D 2020 %T Does AI qualify for the job? A bidirectional model mapping labour and AI intensities %A Mart'nez-Plumed, Fernando %A Tolan, Song'l %A Pesole, Annarosa %A Hern'ndez-Orallo, José %A Fern'ndez-Mac'as, Enrique %A G'mez, Emilia %K AI benchmarks %K AI impact %K AI intensity %K Labour market %K Simulation %K tasks %X In this paper we present a setting for examining the relation between the distribution of research intensity in AI research and the relevance for a range of work tasks (and occupations) in current and simulated scenarios. We perform a mapping between labour and AI using a set of cognitive abilities as an intermediate layer. This setting favours a two-way interpretation to analyse (1) what impact current or simulated AI research activity has or would have on labour-related tasks and occupations, and (2) what areas of AI research activity would be responsible for a desired or undesired effect on specific labour tasks and occupations. Concretely, in our analysis we map 59 generic labour-related tasks from several worker surveys and databases to 14 cognitive abilities from the cognitive science literature, and these to a comprehensive list of 328 AI benchmarks used to evaluate progress in AI techniques. We provide this model and its implementation as a tool for simulations. We also show the effectiveness of our setting with some illustrative examples. %B AIES 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society %P 94–100 %@ 9781450371100 %G eng %R 10.1145/3375627.3375831 %0 Conference Paper %B Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2020 %D 2020 %T Similarity Learning Networks for Animal Individual Re-Identification-Beyond the Capabilities of a Human Observer %A Schneider, Stefan %A Taylor, Graham W. %A Kremer, Stefan C. %X Deep learning has become the standard methodology to approach computer vision tasks when large amounts of labeled data are available. One area where traditional deep learning approaches fail to perform is one-shot learning tasks where a model must correctly classify a new category after seeing only one example. One such domain is animal re-identification, an application of computer vision which can be used globally as a method to automate species population estimates from camera trap images. Our work demonstrates both the application of similarity comparison networks to animal re-identification, as well as the capabilities of deep convolutional neural networks to generalize across domains. Few studies have considered animal re-identification methods across species. Here, we compare two similarity comparison methodologies: Siamese and Triplet-Loss, based on the AlexNet, VGG-19, DenseNet201, MobileNetV2, and InceptionV3 architectures considering mean average precision (mAP)@1 and mAP@5. We consider five data sets corresponding to five different species: Humans, chimpanzees, humpback whales, fruit flies, and Siberian tigers, each with their own unique set of challenges. We demonstrate that Triplet Loss outperformed its Siamese counterpart for all species. Without any species-specific modifications, our results demonstrate that similarity comparison networks can reach a performance level beyond that of humans for the task of animal re-identification. The ability for researchers to re-identify an animal individual upon re-encounter is fundamental for addressing a broad range of questions in the study of population dynamics and community/behavioural ecology. Our expectation is that similarity comparison networks are the beginning of a major trend that could stand to revolutionize animal re-identification from camera trap data. %B Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2020 %V 479 %P 44–52 %@ 9781728171623 %G eng %R 10.1109/WACVW50321.2020.9096925 %0 Journal Article %J The TQM Journal %D 2020 %T Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation %A Theresa, Eriksson %A Alessandro, Bigi %A Michelle, Bonera %A Eriksson, Theresa %A Bigi, Alessandro %A Bonera, Michelle %A Theresa, Eriksson %A Alessandro, Bigi %A Michelle, Bonera %K AI %K artificial intelligence %K creativity %K marketing strategy %K marketing synergy %K paper type research paper %K rationality %K tqm %X 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' organizational model. A key consideration is a “creative-possibility perspective,” 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 “creative-possibility perspective.”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. %B The TQM Journal %V ahead-of-p %8 jan %@ 1754-2731 %G eng %U https://doi.org/10.1108/TQM-12-2019-0303 %R 10.1108/TQM-12-2019-0303 %0 Journal Article %D 2020 %T Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims %A Brundage, Miles %A Avin, Shahar %A Wang, Jasmine %A Belfield, Haydn %A Krueger, Gretchen %A Hadfield, Gillian %A Khlaaf, Heidy %A Yang, Jingying %A Toner, Helen %A Fong, Ruth %A Maharaj, Tegan %A Koh, Pang Wei %A Hooker, Sara %A Leung, Jade %A Trask, Andrew %A Bluemke, Emma %A Lebensold, Jonathan %A O'Keefe, Cullen %A Koren, Mark %A Ryffel, Théo %A Rubinovitz, JB %A Besiroglu, Tamay %A Carugati, Federica %A Clark, Jack %A Eckersley, Peter %A de Haas, Sarah %A Johnson, Maritza %A Laurie, Ben %A Ingerman, Alex %A Krawczuk, Igor %A Askell, Amanda %A Cammarota, Rosario %A Lohn, Andrew %A Krueger, David %A Stix, Charlotte %A Henderson, Peter %A Graham, Logan %A Prunkl, Carina %A Martin, Bianca %A Seger, Elizabeth %A Zilberman, Noa %A HÉigeartaigh, Seán Ó %A Kroeger, Frens %A Sastry, Girish %A Kagan, Rebecca %A Weller, Adrian %A Tse, Brian %A Barnes, Elizabeth %A Dafoe, Allan %A Scharre, Paul %A Herbert-Voss, Ariel %A Rasser, Martijn %A Sodhani, Shagun %A Flynn, Carrick %A Gilbert, Thomas Krendl %A Dyer, Lisa %A Khan, Saif %A Bengio, Yoshua %A Anderljung, Markus %X With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose–spanning institutions, software, and hardware–and make recommendations aimed at implementing, exploring, or improving those mechanisms. %P 1–9 %G eng %U http://arxiv.org/abs/2004.07213 %0 Conference Paper %B International Conference on Exploring Services Science %D 2020 %T Understanding the Impact of Artificial Intelligence on Services %A Ferreira, Pedro %A Teixeira, Jorge Grenha %A Teixeira, Luís F. %E Nóvoa, Henriqueta %E Drăgoicea, Monica %E Kühl, Niklas %K á servitization á design %K science research %K service design %X The service sector is changing drastically due the use of robotics and other technologies, such as Artificial Intelligence (AI), Internet of things (IoT), Big Data and Biometrics. Consequently, further research opportunities in the service industry domain are also expected. In light of the above, the purpose of this paper is to explore the potentialities and limitations of service robots in the hospitality industry. To this end, this paper uses a conceptual approach based on a literature review. As a result, we found that in contexts of high customer contact, service robots should be considered to perform standardized tasks due to social/emotional and cognitive/analytical complexity. The hospitality industry is therefore considered closely related to empathic intelligence, as the integration of service robots has not yet reached the desired stage of service delivery. In a seemingly far-fetched context of our reality, organizations will have to decide whether the AI will allow the complete replacement of humans with robots capable of performing the necessary cognitive and emotional tasks. Or investing in balanced capacities by integrating robot-human systems that seems a rea- sonable option these days. Keywords: %B International Conference on Exploring Services Science %S Lecture Notes in Business Information Processing %I Springer International Publishing %C Cham %V 1 %P 202–213 %@ 9783030387242 %G eng %U http://link.springer.com/10.1007/978-3-030-38724-2 %R 10.1007/978-3-030-38724-2 %0 Journal Article %J Transfer: European Review of Labour and Research %D 2019 %T Algorithms, artificial intelligence and automated decisions concerning workers and the risks of discrimination: the necessary collective governance of data protection %A Todolí-Signes, Adrián %B Transfer: European Review of Labour and Research %V 25 %P 465 - 481 %8 Jan-11-2019 %G eng %N 4 %R 10.1177/1024258919876416 %0 Journal Article %J Drug Safety %D 2019 %T Artificial Intelligence and the Future of the Drug Safety Professional %A Danysz, Karolina %A Cicirello, Salvatore %A Mingle, Edward %A Assuncao, Bruno %A Tetarenko, Niki %A Mockute, Ruta %A Abatemarco, Danielle %A Widdowson, Mark %A Desai, Sameen %B Drug Safety %V 42 %P 491 - 497 %8 Jan-04-2019 %G eng %N 4 %R 10.1007/s40264-018-0746-z %0 Journal Article %J European Journal of Cancer %D 2019 %T A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task %A Brinker, Titus J. %A Hekler, Achim %A Alexander Enk %A Klode, Joachim %A Hauschild, Axel %A Berking, Carola %A Schilling, Bastian %A Haferkamp, Sebastian %A Schadendorf, Dirk %A Fröhling, Stefan %A Jochen Sven Utikal %A von Kalle, Christof %A Ludwig-Peitsch, Wiebke %A Sirokay, Judith %A Heinzerling, Lucie %A Albrecht, Magarete %A Baratella, Katharina %A Bischof, Lena %A Chorti, Eleftheria %A Dith, Anna %A Drusio, Christina %A Giese, Nina %A Gratsias, Emmanouil %A Griewank, Klaus %A Hallasch, Sandra %A Hanhart, Zdenka %A Herz, Saskia %A Hohaus, Katja %A Jansen, Philipp %A Jockenhöfer, Finja %A Kanaki, Theodora %A Knispel, Sarah %A Leonhard, Katja %A Martaki, Anna %A Matei, Liliana %A Matull, Johanna %A Olischewski, Alexandra %A Petri, Maximilian %A Placke, Jan-Malte %A Raub, Simon %A Salva, Katrin %A Schlott, Swantje %A Sody, Elsa %A Steingrube, Nadine %A Stoffels, Ingo %A Ugurel, Selma %A Sondermann, Wiebke %A Zaremba, Anne %A Gebhardt, Christoffer %A Booken, Nina %A Christolouka, Maria %A Buder-Bakhaya, Kristina %A Bokor-Billmann, Therezia %A Alexander Enk %A Gholam, Patrick %A Hänßle, Holger %A Salzmann, Martin %A Schäfer, Sarah %A Schäkel, Knut %A Schank, Timo %A Bohne, Ann-Sophie %A Deffaa, Sophia %A Drerup, Katharina %A Egberts, Friederike %A Erkens, Anna-Sophie %A Ewald, Benjamin %A Falkvoll, Sandra %A Gerdes, Sascha %A Harde, Viola %A Hauschild, Axel %A Jost, Marion %A Kosova, Katja %A Messinger, Laetitia %A Metzner, Malte %A Morrison, Kirsten %A Motamedi, Rogina %A Pinczker, Anja %A Rosenthal, Anne %A Scheller, Natalie %A Schwarz, Thomas %A Stölzl, Dora %A Thielking, Federieke %A Tomaschewski, Elena %A Wehkamp, Ulrike %A Weichenthal, Michael %A Wiedow, Oliver %A Bär, Claudia Maria %A Bender-Säbelkampf, Sophia %A Horbrügger, Marc %A Karoglan, Ante %A Kraas, Luise %A Faulhaber, Jörg %A Geraud, Cyrill %A Guo, Ze %A Koch, Philipp %A Linke, Miriam %A Maurier, Nolwenn %A Müller, Verena %A Thomas, Benjamin %A Jochen Sven Utikal %A Alamri, Ali Saeed M. %A Baczako, Andrea %A Berking, Carola %A Betke, Matthias %A Haas, Carolin %A Hartmann, Daniela %A Heppt, Markus V. %A Kilian, Katharina %A Krammer, Sebastian %A Lapczynski, Natalie Lidia %A Mastnik, Sebastian %A Nasifoglu, Suzan %A Ruini, Cristel %A Sattler, Elke %A Schlaak, Max %A Wolff, Hans %A Achatz, Birgit %A Bergbreiter, Astrid %A Drexler, Konstantin %A Ettinger, Monika %A Haferkamp, Sebastian %A Halupczok, Anna %A Hegemann, Marie %A Dinauer, Verena %A Maagk, Maria %A Mickler, Marion %A Philipp, Biance %A Wilm, Anna %A Wittmann, Constanze %A Gesierich, Anja %A Glutsch, Valerie %A Kahlert, Katrin %A Kerstan, Andreas %A Schilling, Bastian %A Schrüfer, Philipp %B European Journal of Cancer %V 111 %P 148 - 154 %8 Jan-04-2019 %G eng %R 10.1016/j.ejca.2019.02.005 %0 Journal Article %J European Journal of Cancer %D 2019 %T Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task %A Brinker, Titus J. %A Hekler, Achim %A Alexander Enk %A Klode, Joachim %A Hauschild, Axel %A Berking, Carola %A Schilling, Bastian %A Haferkamp, Sebastian %A Schadendorf, Dirk %A Holland-Letz, Tim %A Jochen Sven Utikal %A von Kalle, Christof %A Ludwig-Peitsch, Wiebke %A Sirokay, Judith %A Heinzerling, Lucie %A Albrecht, Magarete %A Baratella, Katharina %A Bischof, Lena %A Chorti, Eleftheria %A Dith, Anna %A Drusio, Christina %A Giese, Nina %A Gratsias, Emmanouil %A Griewank, Klaus %A Hallasch, Sandra %A Hanhart, Zdenka %A Herz, Saskia %A Hohaus, Katja %A Jansen, Philipp %A Jockenhöfer, Finja %A Kanaki, Theodora %A Knispel, Sarah %A Leonhard, Katja %A Martaki, Anna %A Matei, Liliana %A Matull, Johanna %A Olischewski, Alexandra %A Petri, Maximilian %A Placke, Jan-Malte %A Raub, Simon %A Salva, Katrin %A Schlott, Swantje %A Sody, Elsa %A Steingrube, Nadine %A Stoffels, Ingo %A Ugurel, Selma %A Zaremba, Anne %A Gebhardt, Christoffer %A Booken, Nina %A Christolouka, Maria %A Buder-Bakhaya, Kristina %A Bokor-Billmann, Therezia %A Alexander Enk %A Gholam, Patrick %A Hänßle, Holger %A Salzmann, Martin %A Schäfer, Sarah %A Schäkel, Knut %A Schank, Timo %A Bohne, Ann-Sophie %A Deffaa, Sophia %A Drerup, Katharina %A Egberts, Friederike %A Erkens, Anna-Sophie %A Ewald, Benjamin %A Falkvoll, Sandra %A Gerdes, Sascha %A Harde, Viola %A Hauschild, Axel %A Jost, Marion %A Kosova, Katja %A Messinger, Laetitia %A Metzner, Malte %A Morrison, Kirsten %A Motamedi, Rogina %A Pinczker, Anja %A Rosenthal, Anne %A Scheller, Natalie %A Schwarz, Thomas %A Stölzl, Dora %A Thielking, Federieke %A Tomaschewski, Elena %A Wehkamp, Ulrike %A Weichenthal, Michael %A Wiedow, Oliver %A Bär, Claudia Maria %A Bender-Säbelkampf, Sophia %A Horbrügger, Marc %A Karoglan, Ante %A Kraas, Luise %A Faulhaber, Jörg %A Geraud, Cyrill %A Guo, Ze %A Koch, Philipp %A Linke, Miriam %A Maurier, Nolwenn %A Müller, Verena %A Thomas, Benjamin %A Jochen Sven Utikal %A Alamri, Ali Saeed M. %A Baczako, Andrea %A Berking, Carola %A Betke, Matthias %A Haas, Carolin %A Hartmann, Daniela %A Heppt, Markus V. %A Kilian, Katharina %A Krammer, Sebastian %A Lapczynski, Natalie Lidia %A Mastnik, Sebastian %A Nasifoglu, Suzan %A Ruini, Cristel %A Sattler, Elke %A Schlaak, Max %A Wolff, Hans %A Achatz, Birgit %A Bergbreiter, Astrid %A Drexler, Konstantin %A Ettinger, Monika %A Haferkamp, Sebastian %A Halupczok, Anna %A Hegemann, Marie %A Dinauer, Verena %A Maagk, Maria %A Mickler, Marion %A Philipp, Biance %A Wilm, Anna %A Wittmann, Constanze %A Gesierich, Anja %A Glutsch, Valerie %A Kahlert, Katrin %A Kerstan, Andreas %A Schilling, Bastian %A Schrüfer, Philipp %B European Journal of Cancer %V 113 %P 47 - 54 %8 Jan-05-2019 %G eng %R 10.1016/j.ejca.2019.04.001 %0 Journal Article %J Journal of Economic Literature %D 2019 %T Digital Economics %A Goldfarb, Avi %A Tucker, Catherine %B Journal of Economic Literature %V 57 %P 3 - 43 %8 Jan-03-2019 %G eng %N 1 %R 10.1257/jel.20171452 %0 Generic %D 2019 %T Employment Transformation through Artificial Intelligence %A Dheeraj Singh, %A Geetali Tilak %7 International Journal of Applied Engineering Research %G eng %U https://scholar.google.co.in/citations?user=5wxReHAAAAAJ&hl=en#d=gs_md_cita-d&u=%2Fcitations%3Fview_op%3Dview_citation%26hl%3Den%26user%3D5wxReHAAAAAJ%26citation_for_view%3D5wxReHAAAAAJ%3Ad1gkVwhDpl0C%26tzom%3D300 %0 Magazine Article %D 2019 %T How big data is changing the job market %A Editorial Team %B insideBIGDATA %G eng %U https://www.dropbox.com/sh/8sxki5mwikjxlte/AAB6zvW5F0s2FIJ6jUmEW7P-a/Popular%20press%20about%20impacts?dl=0&preview=How+Big+Data+Is+Changing+the+Job+Market.pdf&subfolder_nav_tracking=1 %0 Conference Proceedings %B Electronic Government %D 2019 %T How to Streamline AI Application in Government? A Case Study on Citizen Participation in Germany %A Balta, Dian %A Kuhn, Peter %A Sellami, Mahdi %A Kulus, Daniel %A Lieven, Claudius %A Krcmar, Helmut %E Lindgren, Ida %E Janssen, Marijn %E Lee, Habin %E Polini, Andrea %E Rodríguez Bolívar, Manuel Pedro %E Scholl, Hans Jochen %E Tambouris, Efthimios %X Artificial intelligence (AI) technologies are on the rise in almost every aspect of society, business and government. Especially in government, it is of interest how the application of AI can be streamlined: at least, in a controlled environment, in order to be able to evaluate potential (positive and negative) impact. Unfortunately, reuse in development of AI applications and their evaluation results lack interoperability and transferability. One potential remedy to this challenge would be to apply standardized artefacts: not only on a technical level, but also on an organization or semantic level. This paper presents findings from a qualitative explorative case study on online citizen participation in Germany that reveal insights on the current standardization level of AI applications. In order to provide an in-depth analysis, the research involves evaluation of two particular AI approaches to natural language processing. Our findings suggest that standardization artefacts for streamlining AI application exist predominantly on a technical level and are still limited. %B Electronic Government %I Springer International Publishing %C Cham %V Lecture Notes in Computer Science 11685 %P 233-247 %@ 978-3-030-27324-8 %G eng %R 10.1007/978-3-030-27325-5 %0 Journal Article %J BMC Health Services Research %D 2019 %T Human resource technology disruptions and their implications for human resources management in healthcare organizations %A Tursunbayeva, Aizhan %B BMC Health Services Research %V 19 %8 Jan-12-2019 %G eng %N 1 %R 10.1186/s12913-019-4068-3 %0 Thesis %B Department of Management and Engineering %D 2019 %T Recruiters just wanna have AI? %A Hannimari Savola %A Bijona Troqe %B Department of Management and Engineering %7 Spring semester 2019 %G eng %U https://liu.diva-portal.org/smash/get/diva2:1333711/FULLTEXT01.pdf %0 Conference Paper %B 8th International Conference on Data Science, Technology and ApplicationsProceedings of the 8th International Conference on Data Science, Technology and Applications %D 2019 %T Why small data holds the key to the future of artificial intelligence %A Greco, Ciro %A Polonioli, Andrea %A Tagliabue, Jacopo %B 8th International Conference on Data Science, Technology and ApplicationsProceedings of the 8th International Conference on Data Science, Technology and Applications %I SCITEPRESS - Science and Technology Publications %C Prague, Czech Republic %P 340 - 347 %G eng %R 10.5220/0007956203400347 %0 Journal Article %J Journal of Affective Disorders %D 2018 %T Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review %A Lee, Yena %A Ragguett, Renee-Marie %A Mansur, Rodrigo B. %A Boutilier, Justin J. %A Rosenblat, Joshua D. %A Trevizol, Alisson %A Brietzke, Elisa %A Lin, Kangguang %A Pan, Zihang %A Subramaniapillai, Mehala %A Chan, Timothy C.Y. %A Fus, Dominika %A Park, Caroline %A Musial, Natalie %A Zuckerman, Hannah %A Chen, Vincent Chin-Hung %A Ho, Roger %A Rong, Carola %A McIntyre, Roger S. %B Journal of Affective Disorders %V 241 %P 519-532 %G eng %R 10.1016/j.jad.2018.08.073 %0 Report %D 2018 %T As s essing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour %A Emilia Gómez %A Carlos Castillo %A Vicky Charisi %A Verónica Dahl %A Gustavo Deco %A Blagoj Delipetrev %A Nicole Dewandre %A Miguel Ángel González-Ballester %A Fabien Gouyon %A José Hernández-Orallo %A Perfecto Herrera %A Anders Jonsson %A Ansgar Koene %A Martha Larson %A Ramón López de Mántaras %A Bertin Martens %A Marius Miron %A Rubén Moreno-Bote %A Nuria Oliver %A Antonio Puertas Gallardo %A Heike Schweitzer %A Nuria Sebastian %A Xavier Serra %A Joan Serrà %A Songül Tolan %A Karina Vold %G eng %U https://arxiv.org/ftp/arxiv/papers/1806/1806.03192.pdf %0 Generic %D 2018 %T Automation, taxes and transfers with international rivalry %A Rod Tyers %A Yixiao Zhou %K automation %K global modelling %K income distribution %K taxes %K transfers %B Centre for Applied Macroeconomic Analysis %I Australian National University %G eng %U https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2018-09/44_2018_tyers_zhou.pdf %0 Book %D 2018 %T A critical review of the politics of artificial intelligent machines, alienation and the existential risk threat to America’s labour force %A Wogu, Ikedinachi Ayodele %A Misra, Sanjay %A Assibong, Patrick %A Adewumi, Adewole %A Damasevicius, Robertas %A Maskeliunas, Rytis %E Gervasi, Osvaldo %E Murgante, Beniamino %E Misra, Sanjay %E Stankova, Elena %E Torre, Carmelo M. %E Rocha, Ana Maria A.C. %E Taniar, David %E Apduhan, Bernady O. %E Tarantino, Eufemia %E Ryu, Yeonseung %I Springer International Publishing %C Cham %V 10963 %P 217 - 232 %@ 978-3-319-95170-6 %G eng %R 10.1007/978-3-319-95171-3_18 %0 Journal Article %J European View %D 2018 %T The Future of Work: Robots Cooking Free Lunches? %A Turk, Ziga %B European View %V 17 %P 241 - 241 %8 Jun-10-2020 %G eng %N 2 %R 10.1177/1781685818813010 %0 Journal Article %J Systematic Reviews %D 2018 %T Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR) %A Elaine Beller %A Justin Clark %A Guy Tsafnat %A Clive Adams %A Heinz Diehl %A Hans Lund %A Mourad Ouzzani %A Kristina Thayer %A James Thomas %A Tari Turner %A Jun Xia %A Karen Robinson %A Paul Glasziou %K automation %K Collaboration %K Systematic review %X 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 'Vienna Principles' 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. %B Systematic Reviews %I Springer %8 2018 %G eng %N 77 %9 Review %6 7 %R 10.1186/s13643-018-0740-7 %0 Journal Article %J Systematic Reviews %D 2018 %T Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR) %A Elaine Beller %A Justin Clark %A Guy Tsafnat %A Clive Adams %A Heinz Diehl %A Hans Lund %A Mourad Ouzzani %A Kristina Thayer %A James Thomas %A Tari Turner %A Jun Xia %A Karen Robinson %A Paul Glasziou %K science %B Systematic Reviews %V 7 %8 Jan-12-2018 %G eng %N 1 %R 10.1186/s13643-018-0740-7 %0 Web Page %D 2018 %T Promethean AI uses artificial intelligence to help artists fill out game worlds %A Dean Takahashi %K AI use cases %B venturebeat.com %G eng %U https://venturebeat.com/2018/07/18/promethean-ai-uses-artificial-intelligence-to-help-artists-fill-out-game-worlds/ %0 Journal Article %J Proceedings of the IEEE %D 2018 %T Robot revolution: Myth or reality %A Joel Trussell, H. %B Proceedings of the IEEE %V 106 %P 2095 - 2097 %8 Jan-12-2018 %G eng %N 12 %R 10.1109/JPROC.2018.2877520 %0 Book %D 2018 %T The role of technological progress and structural change in the labour market %A Bosio, Giulio %A Cristini, Annalisa %E Bosio, Giulio %E Minola, Tommaso %E Origo, Federica %E Tomelleri, Stefano %I Springer International Publishing %C Cham %P 15 - 41 %@ 978-3-319-90547-1 %G eng %R 10.1007/978-3-319-90548-8_2 %0 Conference Paper %B the 2018 CHI ConferenceProceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI '18 %D 2018 %T Understanding Chatbot-mediated Task Management %A Toxtli, Carlos %A Monroy-Hernández, Andrés %A Cranshaw, Justin %Y Mandryk, Regan %Y Hancock, Mark %Y Perry, Mark %Y Cox, Anna %B the 2018 CHI ConferenceProceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI '18 %I ACM Press %C Montreal QC, CanadaNew York, New York, USA %P 1 - 6 %@ 9781450356206 %G eng %R 10.1145/317357410.1145/3173574.3173632 %0 Report %D 2017 %T Artificial Intelligence For Social Good %A Gregory D. Hager %A Ann Drobnis %A Fei Fang %A Rayid Ghani %A Amy Greenwald %A Terah Lyons %A David C. Parkes %A Jason Schultz %A Suchi Saria %A Stephen F. Smith %A Milind Tambe %G eng %U https://cra.org/ccc/wp-content/uploads/sites/2/2016/04/AI-for-Social-Good-Workshop-Report.pdf %0 Journal Article %J Science %D 2017 %T Artificial intelligence in research %A Musib, Mrinal %A Wang, Feng %A Tarselli, Michael A. %A Yoho, Rachel %A Yu, Kun-Hsing %A Andrés, Rigoberto Medina %A Greenwald, Noah F. %A Pan, Xubin %A Lee, Chien-Hsiu %A Zhang, Jian %A Dutton-Regester, Ken %A Johnston, Jake Wyatt %A Sharafeldin, Icell Mahmoud %B Science %V 357 %P 28 - 30 %8 Jul-07-2017 %G eng %N 6346 %R 10.1126/science.357.6346.28 %0 Magazine Article %D 2017 %T Artificial Intelligence: The next digital frontier %A Jaccques Bughin %A Eric Hazan %A Sree Ramaswamy %A Michael Chui %A Tera Allas %A Peter Dahlstrom %A Nicolaus Henke %A Monica Trench %B McKinsey Global Institute %G eng %U https://www.mckinsey.com/~/media/McKinsey/Industries/Advanced%20Electronics/Our%20Insights/How%20artificial%20intelligence%20can%20deliver%20real%20value%20to%20companies/MGI-Artificial-Intelligence-Discussion-paper.ashx %0 Conference Paper %B the 2017 ACM ConferenceProceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing - CSCW '17 %D 2017 %T Data tracking in search of workflows %A Holten Møller, Naja L. %A Bjørn, Pernille %A Villumsen, Jonas Christoffer %A Hancock, Tine C. Hansen %A Aritake, Toshimitsu %A Tani, Shigeyuki %Y Lee, Charlotte P. %Y Poltrock, Steve %Y Barkhuus, Louise %Y Borges, Marcos %Y Kellogg, Wendy %B the 2017 ACM ConferenceProceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing - CSCW '17 %I ACM Press %C Portland, Oregon, USANew York, New York, USA %P 2153 - 2165 %@ 9781450343350 %G eng %R 10.1145/2998181.2998296 %0 Journal Article %J Scientific Reports %D 2017 %T A deep learning approach for quantifying tumor extent %A Cruz-Roa, Angel %A Gilmore, Hannah %A Basavanhally, Ajay %A Feldman, Michael %A Ganesan, Shridar %A Shih, Natalie N.C. %A Tomaszewski, John %A González, Fabio A. %A Madabhushi, Anant %B Scientific Reports %V 7 %8 Jan-06-2017 %G eng %N 1 %R 10.1038/srep46450 %0 Journal Article %J Nature %D 2017 %T Dermatologist-level classification of skin cancer with deep neural networks %A Esteva, Andre %A Kuprel, Brett %A Novoa, Roberto A %A Ko, Justin %A Swetter, Susan M %A Blau, Helen M %A Thrun, Sebastian %B Nature %V 542 %P 115–118 %G eng %9 Journal Article %0 Journal Article %J Computers in Human Behavior %D 2017 %T An experimental comparison of Chatbot and Human task partners %A Fryer, Luke K. %A Ainley, Mary %A Thompson, Andrew %A Gibson, Aaron %A Sherlock, Zelinda %B Computers in Human Behavior %V 75 %P 461 - 468 %8 Jan-10-2017 %G eng %R 10.1016/j.chb.2017.05.045 %0 Journal Article %J Classical and Quantum Gravity %D 2017 %T Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science %A Michael Zevin %A Scott Coughlin %A Sara Bahaadini %A Emre Besler %A Neda Rohani %A Sarah Allen %A Miriam Cabero %A Kevin Crowston %A Aggelos Katsaggelos %A Shane Larson %A Tae Kyoung Lee %A Chris Lintott %A Tyson Littenberg %A Andrew Lundgren %A Carsten Oesterlund %A Joshua Smith %A Laura Trouille %A Vicky Kalogera %B Classical and Quantum Gravity %V 34 %P 064003 %G eng %9 Journal Article %R 10.1088/1361-6382/aa5cea %0 Journal Article %J Gale Academic Onefile %D 2017 %T How the internet of people will change the future of work %A Anna Tavis %B Gale Academic Onefile %I Human Resource Planning Society %V 40 %G eng %N 3 %9 People & Strategy %0 Report %D 2017 %T Rapid Evidence Review: Impact of artificial intelligence, robotics and automation technologies on work %A Donald Hislop %A Crispin Coombs %A Stanimira Taneva %A Sarah Barnard %X The CIPD and Loughborough University’s report gathers the evidence and insights on emerging technology at work and explores the ethical implications of how we’re currently adopting new technology. This report creates a foundation for delving deeper into how we can ensure that people remain at the heart of work. The report, Impact of artificial intelligence, robotics and automation technologies on work, focuses on the academic literature published since 2011 and evaluates the state of contemporary knowledge. It focuses on four key questions: What should the technological and occupations focus of the review be? What are the work-related outcomes and mediators from the utilisation of artificial intelligence (AI), robotics and automation technologies (considering both the impact for workers and organisations)? What are the impacts of AI, robotics and automation technologies on professions and society more generally? What are the ethical issues related to the contemporary utilisation of AI, robotics and automation technologies? %I Chartered Institute of Personnel and Development %C London, United Kingdom %G eng %U https://www.cipd.co.uk/knowledge/work/technology/artificial-intelligence-workplace-impact %0 Journal Article %J The Journal of Strategic Information Systems %D 2017 %T The strategic opportunities and challenges of algorithmic decision-making %A Galliers, R.D. %A Newell, S. %A Shanks, G. %A Topi, H. %B The Journal of Strategic Information Systems %V 26 %P 185 - 190 %8 Jan-09-2017 %G eng %N 3 %R 10.1016/j.jsis.2017.08.002 %0 Journal Article %J ACM Computing Surveys %D 2017 %T Understanding human-machine networks %A Tsvetkova, Milena %A Yasseri, Taha %A Meyer, Eric T. %A Pickering, J. Brian %A Engen, Vegard %A Walland, Paul %A Lüders, Marika %A Følstad, Asbjørn %A Bravos, George %B ACM Computing Surveys %V 50 %P 1 - 35 %8 Jan-04-2018 %G eng %N 1 %R 10.1145/3039868 %0 Generic %D 2016 %T The Future of Jobs: Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution %A The World Economic Forum %I World Economic Forum %G eng %9 Report %0 Magazine Article %D 2016 %T How artificial intelligence will redefine management %A Vegard Kolbjornsrud %A Richard Amico %A Robert J. Thomas %B Havard Business Review %8 11/2016 %G eng %U https://hbr.org/2016/11/how-artificial-intelligence-will-redefine-management %0 Journal Article %J Journal of latex class files %D 2015 %T Norms, institutions and robots %A Stevan Tomic %A Federico Pecora %A Alessandro Saffotti %B Journal of latex class files %V 14 %8 08/2015 %G eng %U https://arxiv.org/abs/1807.11456 %6 8 %0 Journal Article %J Association for the Advancement of Artificial Intelligence %D 2015 %T Research Priorities for Robust and Beneficial Artificial Intelligence %A Stuart Russell %A Daniel Dewey %A Max Tegmark %X

Artificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents - systems that perceive and act in some environment. In this context, the criterion for intelligence is related to statistical and economic notions of rationality - colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic representations and statistical learning methods can led to a large degree of integration and crossfertilization between AI, machine learning, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems.

%B Association for the Advancement of Artificial Intelligence %V 4 %P 105-114 %G eng %U https://futureoflife.org/data/documents/research_priorities.pdf %0 Report %D 2013 %T Beyond convergence of Nano-Bio-Info-Cognitive technologies %A Mihail C. Roco %A William S. Bainbridge %A Bruce Tonn %A George Whitesides %B WTEC Study on Convergence of Knowledge, Technology, and Society %8 07/2013 %G eng %U http://www.wtec.org/NBIC2/Docs/FinalReport/Pdf-secured/NBIC2-FinalReport-WEB.pdf %0 Journal Article %J Journal of Autism and Developmental Disorders %D 2009 %T Under diagnosis and referral bias of autism in ethnic minorities %A Begeer, Sander %A Bouk, Saloua El %A Boussaid, Wafaa %A Terwogt, Mark Meerum %A Koot, Hans M. %B Journal of Autism and Developmental Disorders %V 39 %P 142 - 148 %8 Jan-01-2009 %G eng %N 1 %R 10.1007/s10803-008-0611-5 %0 Conference Paper %B the ACM 2008 conferenceProceedings of the ACM 2008 conference on Computer supported cooperative work - CSCW '08 %D 2008 %T Context-linked virtual assistants for distributed teams %A Poon, Sarah S. %A Thomas, Rollin C. %A Aragon, Cecilia R. %A Lee, Brian %Y Begole, Bo %Y McDonald, David W. %B the ACM 2008 conferenceProceedings of the ACM 2008 conference on Computer supported cooperative work - CSCW '08 %I ACM Press %C San Diego, CA, USANew York, New York, USA %P 361 %@ 9781605580074 %G eng %R 10.1145/1460563.1460623 %0 Conference Paper %B Proceedings of the ACM Conference on Computer Supported Cooperative Work %D 2008 %T Context-linked virtual assistants for distributed teams: an astrophysics case study %A Poon, Sarah S %A Thomas, Rollin C %A Cecilia Aragon %A Lee, Brian %B Proceedings of the ACM Conference on Computer Supported Cooperative Work %P 361–370 %@ 1605580074 %G eng %9 Conference Proceedings %0 Report %D 2003 %T A Preliminary Analysis of Occupational Task Statements from the O*NET Data Collection Program %A Van Iddekinge, Chad %A Suzanne Tsacoumis %A Jamie Donsbach %I National Center for O*NET Development %@ FR-02-52 %G eng %0 Conference Proceedings %B International Conference on Human-Computer Interaction (HCI International) %D 1995 %T Integration of people, technology and organization: the european approach %A Christina Kirsch %A Peter Troxier %A Eberhard Ulich %X This paper presents the general outline of new method, HITOP-D, considering the integration and joint optimization of people, technology, and organization. This method is based on the existing american methods HITOP and ACTION. It takes in account the specific european industrial context. In an iterating process the preliminary design of a project is assessed according a list of criteria of the four aspects people, technology, organization, and task design. Incongruencies are solved through a fit analysis and redesigning the original project. The performance of HITOP-D will be empirically evaluated. %B International Conference on Human-Computer Interaction (HCI International) %I Elsevier %C Tokyo, Japan %V 20 %P 957-961 %G eng %0 Book Section %B Perspectives in Organization Design and Behavior %D 1981 %T The socio-technical perspective: The evolution of socio-technical systems as a conceptual framework and action research program %A Trist, E. %E van de Ven, A.H. %E Joyce, W. F. %B Perspectives in Organization Design and Behavior %I John Wiley %C London %P 32–47 %G eng %0 Journal Article %J Human relationsHuman Relations %D 1951 %T Some social and psychological consequences of the Longwall Method of coal-getting: An examination of the psychological situation and defences of a work group in relation to the social structure and technological content of the work system %A Trist, Eric Lansdowne %A Bamforth, Ken W %B Human relationsHuman Relations %V 4 %P 3-38 %@ 0018-7267 %G eng %0 Book %D 1911 %T The Principles of Scientific Management %A Taylor, F. W. %K Productionmanagement %I Norton & Company %C New York %G eng