%0 Conference Proceedings %B CHI Conference on Human Factors in Computing Systems %D 2024 %T ReelFramer: Human-AI Co-Creation for News-to-Video Translation %A Sitong Wang %A Samia Menon %A Tao Long %A Keren Henderson %A Dingzeyu Li %A Kevin Crowston %A Mark Hansen %A Jeffrey V. Nickerson %A Lydia B. Chilton %X

Short videos on social media are the dominant way young people consume content. News outlets aim to reach audiences through news reels -- short videos conveying news -- but struggle to translate traditional journalistic formats into short, entertaining videos. To translate news into social media reels, we support journalists in reframing the narrative. In literature, narrative framing is a high-level structure that shapes the overall presentation of a story. We identified three narrative framings for reels that adapt social media norms but preserve news value, each with a different balance of information and entertainment. We introduce ReelFramer, a human-AI co-creative system that helps journalists translate print articles into scripts and storyboards. ReelFramer supports exploring multiple narrative framings to find one appropriate to the story. AI suggests foundational narrative details, including characters, plot, setting, and key information. ReelFramer also supports visual framing; AI suggests character and visual detail designs before generating a full storyboard. Our studies show that narrative framing introduces the necessary diversity to translate various articles into reels, and establishing foundational details helps generate scripts that are more relevant and coherent. We also discuss the benefits of using narrative framing and foundational details in content retargeting.

%B CHI Conference on Human Factors in Computing Systems %C Honolulu, Hawai'i %G eng %U https://arxiv.org/abs/2304.09653 %0 Conference Proceedings %B Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems %D 2023 %T AngleKindling: Supporting Journalistic Angle Ideation with Large Language Models %A Petridis, Savvas %A Diakopoulos, Nicholas %A Crowston, Kevin %A Hansen, Mark %A Henderson, Keren %A Jastrzebski, Stan %A Nickerson, Jefrey V %A Chilton, Lydia B %B Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems %G eng %U https://savvaspetridis.github.io/papers/anglekindling.pdf %R 10.1145/3544548.3580907 %0 Generic %D 2020 %T Automation Technologies and Employment at Risk : The Case of Mexico %A Cebreros, Alfonso %A Heffner-Rodríguez, Aldo %A Livas, René %A Puggioni, Daniela %I Banco de México %G eng %0 Journal Article %J AI %D 2020 %T Cities of the Future? The Potential Impact of Artificial Intelligence %A Kassens-Noor, Eva %A Hintze, Arend %K artificial intelligence %K autonomous vehicle %K future %K smart cities %K work %X Artificial intelligence (AI), like many revolutionary technologies in human history, will have a profound impact on societies. From this viewpoint, we analyze the combined effects of AI to raise important questions about the future form and function of cities. Combining knowledge from computer science, urban planning, and economics while reflecting on academic and business perspectives, we propose that the future of cities is far from being a determined one and cities may evolve into ghost towns if the deployment of AI is not carefully controlled. This viewpoint presents a fundamentally different argument, because it expresses a real concern over the future of cities in contrast to the many publications who exclusively assume city populations will increase predicated on the neoliberal urban growth paradigm that has for centuries attracted humans to cities in search of work. %B AI %V 1 %P 192–197 %G eng %R 10.3390/ai1020012 %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 Journal Article %J Journal of Service Research %D 2020 %T Engaged to a Robot? The Role of AI in Service %A Huang, Ming Hui %A Rust, Roland T %K artificial intelligence %K augmentation %K automation %K engagement %K feeling AI %K human intelligence %K mechanical AI %K personalization %K relationalization %K replacement %K robots %K service process %K service strategy %K standardization %K thinking AI %X This article develops a strategic framework for using artificial intelligence (AI) to engage customers for different service benefits. This framework lays out guidelines of how to use different AIs to engage customers based on considerations of nature of service task, service offering, service strategy, and service process. AI develops from mechanical, to thinking, and to feeling. As AI advances to a higher intelligence level, more human service employees and human intelligence (HI) at the intelligence levels lower than that level should be used less. Thus, at the current level of AI development, mechanical service should be performed mostly by mechanical AI, thinking service by both thinking AI and HI, and feeling service mostly by HI. Mechanical AI should be used for standardization when service is routine and transactional, for cost leadership, and mostly at the service delivery stage. Thinking AI should be used for personalization when service is data-rich and utilitarian, for quality leadership, and mostly at the service creation stage. Feeling AI should be used for relationalization when service is relational and high touch, for relationship leadership, and mostly at the service interaction stage. We illustrate various AI applications for the three major AI benefits, providing managerial guidelines for service providers to leverage the advantages of AI as well as future research implications for service researchers to investigate AI in service from modeling, consumer, and policy perspectives. %B Journal of Service Research %G eng %R 10.1177/1094670520902266 %0 Journal Article %J Artificial Intelligence and Law %D 2020 %T Explainable AI under contract and tort law: legal incentives and technical challenges %A Hacker, Philipp %A Krestel, Ralf %A Grundmann, Stefan %A Naumann, Felix %K Contract law %K Corporate takeovers %K Explainability %K Explainability-accuracy trade-off %K Explainable AI %K Interpretable machine learning %K Medical malpractice %K Tort law %X 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. %B Artificial Intelligence and Law %I Springer Netherlands %@ 0123456789 %G eng %U https://doi.org/10.1007/s10506-020-09260-6 %R 10.1007/s10506-020-09260-6 %0 Conference Proceedings %B Hawai'i International Conference on System Science %D 2020 %T The Genie in the Bottle: Different Stakeholders, Different Interpretations of Machine Learning %A Mahboobeh Harandi %A Kevin Crowston %A Corey Jackson %A Carsten Østerlund %X

Machine learning (ML) constitute an algorithmic phenomenon with some distinctive characteristics (e.g., being trained, probabilistic). Our understanding of such systems is limited when it comes to how these unique characteristics play out in organizational settings and what challenges different groups of users will face in working with them. We explore how people developing or using an ML system come to understand its capabilities and challenges. We draw on the social construction of technology tradition to frame our analysis of interviews and discussion board posts involving designers and users of a ML-supported citizen-science crowdsourcing project named Gravity Spy. Our findings reveal some of the challenges facing different relevant social groups. We find that groups with less interaction with the technology have their understanding. We find that the type of understandings achieved by groups having less interaction with the technology is shaped by outside influences rather than the specifics of the system and its role in the project. Notable, some users mistake human input for ML input. This initial understanding of how different participants understand and engage with ML point to challenges that need to be overcome to help participants deal with the opaque position ML often hold in a work system.

%B Hawai'i International Conference on System Science %C Wailea, HI %G eng %9 Working paper %R 10.24251/HICSS.2020.719 %> https://waim.network/sites/crowston.syr.edu/files/Social_Construction_of_ML_in_GS_HICCS2020.pdf %0 Journal Article %J Academic Radiology %D 2020 %T How the FDA regulates AI %A Harvey, H. Benjamin %A Gowda, Vrushab %B Academic Radiology %V 27 %P 58 - 61 %8 Jan-01-2020 %G eng %N 1 %R 10.1016/j.acra.2019.09.017 %0 Journal Article %J Bulletin of the World Health Organization %D 2020 %T How to achieve trustworthy artificial intelligence for health %A Bærøe, Kristine %A Miyata-Sturm, Ainar %A Henden, Edmund %X Artificial intelligence holds great promise in terms of beneficial, accurate and effective preventive and curative interventions. At the same time, there is also awareness of potential risks and harm that may be caused by unregulated developments of artificial intelligence. Guiding principles are being developed around the world to foster trustworthy development and application of artificial intelligence systems. These guidelines can support developers and governing authorities when making decisions about the use of artificial intelligence. The HighLevel Expert Group on Artificial Intelligence set up by the European Commission launched the report Ethical guidelines for trustworthy artificial intelligence in 2019. The report aims to contribute to reflections and the discussion on the ethics of artificial intelligence technologies also beyond the countries of the European Union (EU). In this paper, we use the global health sector as a case and argue that the EU's guidance leaves too much room for local, contextualized discretion for it to foster trustworthy artificial intelligence globally. We point to the urgency of shared globalized efforts to safeguard against the potential harms of artificial intelligence technologies in health care. %B Bulletin of the World Health Organization %V 98 %P 257–262 %G eng %R 10.2471/BLT.19.237289 %0 Journal Article %J Computer Supported Cooperative Work: CSCW: An International Journal %D 2020 %T The Role of Discretion in the Age of Automation %A Petersen, Anette C.M. %A Christensen, Lars Rune %A Hildebrandt, Thomas T. %K Administrative work %K automation %K Casework %K Decision-Making %K Digital-ready legislation %K Digitisation %K Discretion %K Rules in action %K Social work %X 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. %B Computer Supported Cooperative Work: CSCW: An International Journal %V 29 %P 303–333 %G eng %R 10.1007/s10606-020-09371-3 %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 Journal Article %J Business & Information Systems Engineering %D 2019 %T AI-Based Digital Assistants %A Maedche, Alexander %A Legner, Christine %A Benlian, Alexander %A Berger, Benedikt %A Gimpel, Henner %A Hess, Thomas %A Hinz, Oliver %A Morana, Stefan %A Söllner, Matthias %B Business & Information Systems Engineering %V 61 %P 535 - 544 %8 Jan-08-2019 %G eng %N 4 %R 10.1007/s12599-019-00600-8 %0 Magazine Article %D 2019 %T As Walmart turns to robots, it’s the human workers who feel like machines %A Drew Harwell %B The Washington Post %8 06/2019 %G eng %U https://www.washingtonpost.com/technology/2019/06/06/walmart-turns-robots-its-human-workers-who-feel-like-machines/ %9 Technology %0 Conference Paper %B Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI '19 %D 2019 %T Beyond Dyadic Interactions: Considering Chatbots as Community Members %A Seering, Joseph %A Luria, Michal %A Kaufman, Geoff %A Hammer, Jessica %Y Brewster, Stephen %Y Fitzpatrick, Geraldine %Y Cox, Anna %Y Kostakos, Vassilis %B Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI '19 %I ACM Press %C Glasgow, Scotland UK %P 1-13 %@ 9781450359702 %G eng %R 10.1145/3290605 %0 Journal Article %J Computer %D 2019 %T Bots Coordinating Work in Open Source Software Projects %A Hukal, Philipp %A Berente, Nicholas %A Germonprez, Matt %A Schecter, Aaron %B Computer %V 52 %P 52 - 60 %8 Jan-09-2019 %G eng %N 9 %R 10.1109/MC.2018.2885970 %0 Journal Article %J California Management Review %D 2019 %T A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence %A Haenlein, Michael %A Kaplan, Andreas %B California Management Review %V 61 %P 5 - 14 %8 Sep-08-2019 %G eng %N 4 %R 10.1177/0008125619864925 %0 Journal Article %J European Journal of Cancer %D 2019 %T Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark %A Brinker, Titus J. %A Hekler, Achim %A Hauschild, Axel %A Berking, Carola %A Schilling, Bastian %A Alexander Enk %A Haferkamp, Sebastian %A Karoglan, Ante %A von Kalle, Christof %A Weichenthal, Michael %A Sattler, Elke %A Schadendorf, Dirk %A Gaiser, Maria R. %A Klode, Joachim %A Jochen Sven Utikal %B European Journal of Cancer %V 111 %P 30 - 37 %8 Jan-04-2019 %G eng %R 10.1016/j.ejca.2018.12.016 %0 Conference Paper %B 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) %D 2019 %T Consider the human work experience when integrating robotics in the workplace %A Welfare, Katherine S. %A Hallowell, Matthew R. %A Shah, Julie A. %A Riek, Laurel D. %B 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) %I IEEE %C Daegu, Korea (South) %P 75 - 84 %G eng %U https://ieeexplore.ieee.org/document/8673139/http://xplorestaging.ieee.org/ielx7/8666012/8673065/08673139.pdf?arnumber=8673139 %R 10.1109/HRI.2019.8673139 %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 Book %D 2019 %T Educating those most impacted by artificial intelligence %A Gemmell, Laura %A Wenham, Lucy %A Hauert, Sabine %E Isotani, Seiji %E Millán, Eva %E Ogan, Amy %E Hastings, Peter %E McLaren, Bruce %E Luckin, Rose %I Springer International Publishing %C Cham %V 11626 %P 344 - 349 %@ 978-3-030-23206-1 %G eng %R 10.1007/978-3-030-23207-8_63 %0 Book %D 2019 %T The emergence of complexity %A Hager, Paul %A Beckett, David %I Springer International Publishing %C Cham %@ 978-3-030-31837-6 %G eng %R 10.1007/978-3-030-31839-0 %0 Journal Article %J Asia-pacific Journal of Convergent Research Interchange %D 2019 %T An Extensive Review on Recent Emerging Applications of Artificial Intelligence %A Harini, B %B Asia-pacific Journal of Convergent Research Interchange %V 5 %P 79 - 88 %8 Jun-06-2021 %G eng %U http://www.apjcri.org/papers/v5n2/9.pdf %N 2 %R 10.21742/apjcri10.21742/apjcri.2019.0610.21742/apjcri.2019.06.09 %0 Journal Article %J Journal of Information Systems and Technology Management %D 2019 %T The future digital work force: Robotic process automation (RPA) %A Madakam, Somayya %A M. Holmukhe, Rajesh %A Kumar Jaiswal, Durgesh %B Journal of Information Systems and Technology Management %V 16 %8 Apr-01-2019 %G eng %R 10.4301/S1807-1775201916001 %0 Report %D 2019 %T The future of the work in America %A Susan Lund %A James Manyika %A Liz Hilton Segal %A Andre Dua %A Bryan Hancock %A Scott Rutherford %A Brent Macon %K consulting reports %B McKinsey Global Institute %8 07/2019 %G eng %U https://www.mckinsey.com/featured-insights/future-of-work/the-future-of-work-in-america-people-and-places-today-and-tomorrow %0 Generic %D 2019 %T The future of women at work %A Anu Madgavkar %A James Manyika %A Mekala Krishnan %A Kweilin Ellingrud %A Lareina Yee %A Jonathan Woetzel %A Michael Chui %A Vivian Hunt %A Sruti Balakrishnan %K consulting reports %V McKinsey Global Institute %8 06/2019 %G eng %U 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 %0 Magazine Article %D 2019 %T Future of work initiatives promise lots of noise and lots of activity, but to what end? %A Jeff Schwartz %A John Hagel III %A Maggie Wooll %A Kelly Monahan %B MIT Slogan Management Review %G eng %U https://sloanreview.mit.edu/article/reframing-the-future-of-work/ %0 Journal Article %J Foresight and STI Governance %D 2019 %T Getting ready for a post-work future %A Hines, Andy %B Foresight and STI Governance %V 13 %P 19 - 30 %8 Jun-03-2021 %G eng %N 1 %R 10.17323/2500-2597.2019.1.19.30 %0 Conference Paper %B Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences %D 2019 %T How May I Help You? – State of the Art and Open Research Questions for Chatbots at the Digital Workplace %A Meyer von Wolff, Raphael %A Hobert, Sebastian %A Schumann, Matthias %Y Bui, Tung %B Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences %I Hawaii International Conference on System Sciences %@ 978-0-9981331-2-6 %G eng %R 10.24251/HICSS.2019.013 %0 Conference Paper %B the 2019 AAAI/ACM ConferenceProceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society - AIES '19 %D 2019 %T Inferring work task automatability from AI expert evidence %A Duckworth, Paul %A Graham, Logan %A Osborne, Michael %Y Conitzer, Vincent %Y Hadfield, Gillian %Y Vallor, Shannon %B the 2019 AAAI/ACM ConferenceProceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society - AIES '19 %I ACM Press %C Honolulu, HI, USANew York, New York, USA %P 485 - 491 %@ 9781450363242 %G eng %R 10.1145/330661810.1145/3306618.3314247 %0 Book Section %B researchgate.net %D 2019 %T Machine learning for clinical psychology and clinical neuroscience %A Marc N. Countanche %A Lauren S. Hallion %X A rapid growth in computational power and an increasing availability of large, publicly-accessible, multimodal datasets present new opportunities for psychology and neuroscience researchers to ask novel questions, and to approach old questions in novel ways. Studies of the personal characteristics, situation-specific factors, and sociocultural contexts that result in the onset, development, maintenance, and remission of psychopathology, are particularly well-suited to benefit from machine learning methods. However, introductory textbooks for machine learning rarely tailor their guidance to the needs of psychology and neuroscience researchers. Similarly, the traditional statistical training of clinical scientists often does not incorporate these approaches. This chapter acts as an introduction to machine learning for researchers in the fields of clinical psychology and clinical neuroscience. We discuss these methods, illustrated through real and hypothetical applications in the fields of clinical psychology and clinical neuroscience. We touch on study design, selecting appropriate techniques, how (and how not) to interpret results, and more, to aid researchers who are interested in applying machine learning methods to clinical science data. %B researchgate.net %G eng %U https://www.researchgate.net/publication/331000572_Machine_Learning_for_Clinical_Psychology_and_Clinical_Neuroscience %0 Conference Paper %B Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences %D 2019 %T Machine learning in Artificial Intelligence: Towards a common understanding %A Kühl, Niklas %A Goutier, Marc %A Hirt, Robin %A Satzger, Gerhard %Y Bui, Tung %B Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences %I Hawaii International Conference on System Sciences %G eng %R 10.24251/HICSS.2019.630 %0 Journal Article %J Journal of Pediatric Nursing %D 2019 %T Nurses' Views on the Potential Use of Robots in the Pediatric Unit %A Liang, Hwey-Fang %A Wu, Kuang-Ming %A Weng, Cheng-Hsing %A Hsieh, Hui-Wen %B Journal of Pediatric Nursing %V 47 %P e58 - e64 %8 Jan-07-2019 %G eng %R 10.1016/j.pedn.2019.04.027 %0 Generic %D 2019 %T A Review of the Social Impacts and Ethical Implications of Artificial Intelligence %A Alexa Hagerty %A Igor Rubinov %K ethics %I Global AI Ethics %G eng %U https://arxiv.org/ftp/arxiv/papers/1907/1907.07892.pdf %0 Journal Article %J Proceedings of the National Academy of Sciences %D 2019 %T Scaling up analogical innovation with crowds and AI %A Kittur, Aniket %A Yu, Lixiu %A Hope, Tom %A Chan, Joel %A Lifshitz-Assaf, Hila %A Gilon, Karni %A Ng, Felicia %A Kraut, Robert E. %A Shahaf, Dafna %B Proceedings of the National Academy of Sciences %V 116 %P 1870-1877 %G eng %N 6 %R 10.1073/pnas.1807185116 %0 Report %D 2019 %T Towards an AI economy that works for all %A Stephen Herzenberg %A John Alic %K ethics %B Keystone Research Center Future of Work Project sponsored by The Heinz Endowments %8 02/2019 %G eng %U https://www.keystoneresearch.org/sites/default/files/FOW_TowardAIEconomyForAllFinalEdit.pdf %0 Journal Article %J Network Intelligence Studies %D 2019 %T Will artificial intelligence take over human resources recruitment and selection? %A Bilal Hmoud %A Varallya Laszlo %K artificial intelligence %K human resources information systems %K recruitment and selection %B Network Intelligence Studies %V VII %G eng %U https://ideas.repec.org/a/cmj/networ/y2019i13p21-30.html %N 13 %0 Journal Article %J data-efficient ML %D 2018 %T Accounting for the neglected dimensions of AI progress %A Fernando Martine-Plumed %A Shahar Avin %A Miles Brundage %A Allan Dafoe %A Sean O hEigeartaigh %A José Hernández-Orallo %B data-efficient ML %G eng %U https://arxiv.org/abs/1806.00610 %0 Journal Article %J MATEC Web of Conferences %D 2018 %T Advances in the development of a cognitive user interface %A Jokisch, Oliver %A Huber, Markus %E Ronzhin, A. %E Shishlakov, V. %B MATEC Web of Conferences %V 161 %P 01003 %8 Jan-01-2018 %G eng %U https://www.matec-conferences.org/10.1051/matecconf/201816101003https://www.matec-conferences.org/10.1051/matecconf/201816101003/pdf %R 10.1051/matecconf/201816101003 %0 Web Page %D 2018 %T AI Could Provide Moment-by-Moment Nursing for a Hospital’s Sickest Patients %A Behnood Gholami %A Wassim M. Haddad %A James M. Bailey %B spectrum.ieee.org %G eng %U https://globaltechnologies.ca/ai-could-provide-moment-by-moment-nursing-for-a-hospitals-sickest-patients/ %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 Journal Article %D 2018 %T Artificial intelligence can transform the economy %A Erik Brynjolfsson %A Xiang Hui %A Meng Liu %8 09/2018 %G eng %U https://www.washingtonpost.com/opinions/artificial-intelligence-can-transform-the-economy/2018/09/18/50c9c9c8-bab8-11e8-bdc0-90f81cc58c5d_story.html %0 Journal Article %J Risk Management and Insurance Review %D 2018 %T Artificial intelligence: Implications for social inflation and insurance %A Kelley, Kevin H. %A Fontanetta, Lisa M. %A Heintzman, Mark %A Pereira, Nikki %B Risk Management and Insurance Review %V 21 %P 373 - 387 %8 Jan-12-2018 %G eng %N 3 %R 10.1111/rmir.12111 %0 Journal Article %J Journal of Service Research %D 2018 %T Artificial Intelligence in Service %A Huang, Ming-Hui %A Rust, Roland T. %B Journal of Service Research %V 21 %P 155 - 172 %8 May-05-2018 %G eng %N 2 %R 10.1177/1094670517752459 %0 Journal Article %J British Journal of Radiology %D 2018 %T Artificial intelligence, machine learning and the evolution of healthcare A bright future or cause for concern? %A L.D. Jones %A D. Golan %A S. A. Hanna %A M. Ramachandran %K artificial intelligence %K deep learning %K machine learning %B British Journal of Radiology %V 7 %P 223-225 %G eng %N 7 %9 Editorial %0 Journal Article %J Bone & Joint Research %D 2018 %T Artificial intelligence, machine learning and the evolution of healthcare %A Jones, L. D. %A D. Golan %A S. A. Hanna %A M. Ramachandran %B Bone & Joint Research %V 7 %P 223 - 225 %8 Jan-03-2018 %G eng %N 3 %R 10.1302/2046-3758.73.BJR-2017-0147.R1 %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 Report %D 2018 %T Autonomous vehicles employment impact study %A Brian Haratsis %A Tony Carmichael %A Mark Coutney %A Jacob Fong %B Australia & New Zealand Driverless Vehicle Initiate %8 09/2018 %G eng %U https://advi.org.au/wp-content/uploads/2018/09/Autonomous-Vehicles-Employment-Impact-Survey-COR050918-5.pdf %0 Journal Article %J Sustainability Science %D 2018 %T Boundary spanning at the science–policy interface: the practitioners’ perspectives %A Bednarek, A. T. %A Wyborn, C. %A Cvitanovic, C. %A Meyer, R. %A Colvin, R. M. %A Addison, P. F. E. %A Close, S. L. %A Curran, K. %A Farooque, M. %A Goldman, E. %A Hart, D. %A Mannix, H. %A McGreavy, B. %A Parris, A. %A Posner, S. %A Robinson, C. %A Ryan, M. %A Leith, P. %B Sustainability Science %V 13 %P 1175 - 1183 %8 Jan-07-2018 %G eng %N 4 %R 10.1007/s11625-018-0550-9 %0 Journal Article %J Strategic HR Review %D 2018 %T Can artificial intelligence make work more human? %A He, Emily %B Strategic HR Review %V 17 %P 263 - 264 %8 Aug-10-2018 %G eng %N 5 %R 10.1108/SHR-10-2018-146 %0 Journal Article %J Journal of Monetary Economics %D 2018 %T Comment on “Should we fear the robot revolution? (The correct answer is yes)” by Andrew Berg, Ed Buffie, and Felipe Zanna %A Hanley, Douglas %B Journal of Monetary Economics %V 97 %P 149 - 152 %8 Jan-08-2018 %G eng %R 10.1016/j.jmoneco.2018.05.012 %0 Journal Article %J SSRN Electronic Journal %D 2018 %T Computational power and the social impact of artificial intelligence %A Tim Hwang %X Machine learning is computational process. To that end, it is inextricably tied to computational power - the tangible material of chips and semiconductors that the algorithms of machine intelligence operate on. Most obviously, computational power and computing architectures shape the speed of training and inference in machine learning, and therefore influence the rate of progress in the technology. But, these relationships are more nuanced than that: hardware shapes the methods used by researchers and engineers in the design and development of machine learning models. Characteristics such as the power consumption of chips also define where and how machine learning can be used in the real world. In a broader perspective, computational power is also important because of its specific geographies. Semiconductors are designed, fabricated, and deployed through a complex international supply chain. Market structure and competition among companies in this space influence the progress of machine learning. Moreover, since these supply chains are also considered significant from a national security perspective, hardware becomes an arena in which government industrial and trade policy has a direct impact on the fundamental machinery necessary for artificial intelligence (AI). This paper aims to dig more deeply into the relationship between computational power and the development of machine learning. Specifically, it examines how changes in computing architectures, machine learning methodologies, and supply chains might influence the future of AI. In doing so, it seeks to trace a set of specific relationship between this underlying hardware layer and the broader social impacts and risks around AI. On the hand, this examination shines a spotlight on how hardware works to exacerbate a range of concerns around ubiquitous surveillance, technological unemployment, and geopolitical conflict. On the other, it also highlights the potentially significant role that shaping the development of computing power might play in addressing these concerns. %B SSRN Electronic Journal %G eng %R 10.2139/ssrn.3147971 %0 Journal Article %J Journal of the Association for Information Systems %D 2018 %T The discourse approach to boundary identification and corpus construction for theory review articles %A Kai R. Larsen %A Dirik S. Hovorka %A Alan R. Dennis %A Jevin D. West %K article identification %K boundary identification %K citation search %K keyword search %K literature review %K machine learning %K research review %K review article %B Journal of the Association for Information Systems %G eng %U https://www.researchgate.net/publication/325215971_Understanding_the_Elephant_The_Discourse_Approach_to_Boundary_Identification_and_Corpus_Construction_for_Theory_Review_Articles %0 Journal Article %J Journal of Monetary Economics %D 2018 %T Discussion for JME special issue: APST paper %A Hershbein, Brad %B Journal of Monetary Economics %V 97 %P 68 - 70 %8 Jan-08-2018 %G eng %R 10.1016/j.jmoneco.2018.05.003 %0 Book %D 2018 %T eHealth - Making Health Care SmarterUse of Artificial Intelligence in Healthcare Delivery %A Reddy, Sandeep %E Heston, Thomas F. %I InTech %@ 978-1-78923-522-7 %G eng %R http://dx.doi.org/10.5772/intechopen.74714 %0 Journal Article %J Texas A&M Journal of Property Law %D 2018 %T Ethics of using Artificial Intelligence to augment drafting legal documents %A David Hricik Hri %A Asya-Lorrene S. Morgan %A Kyle H. Williams %B Texas A&M Journal of Property Law %7 3 %V 4 %G eng %U https://scholarship.law.tamu.edu/cgi/viewcontent.cgi?article=1080&context=journal-of-property-law %N 5 %0 Journal Article %J The Information Society %D 2018 %T Exploiting ability for human adaptation to facilitate improved human-robot interaction and acceptance %A Caleb-Solly, Praminda %A Dogramadzi, Sanja %A Huijnen, Claire A.G.J. %A Heuvel, Herjan van den %B The Information Society %V 34 %P 153 - 165 %8 Mar-05-2020 %G eng %N 3 %R 10.1080/01972243.2018.1444255 %0 Journal Article %J Communications of the Association for Information Systems %D 2018 %T How do machine learning, robotic process automation, and blockchains affect the human factor in business process management? %A Mendling, Jan %A Decker, Gero %A Hull, Richard %A Reijers, Hajo A. %A Weber, Ingo %B Communications of the Association for Information Systems %P 297 - 320 %8 Jan-01-2018 %G eng %R 10.17705/1CAIS.04319 %0 Journal Article %J International Journal of Global Sustainability %D 2018 %T The impact of technology and globalization on employment and equity %A Arogyaswamy, Bernard %A Hunter, John %B International Journal of Global Sustainability %V 3 %P 49 %8 Oct-11-2019 %G eng %N 1 %R 10.5296/ijgs.v3i1.14127 %0 Journal Article %J BMC Medical Informatics and Decision Making %D 2018 %T Improving palliative care with deep learning %A Avati, Anand %A Jung, Kenneth %A Harman, Stephanie %A Downing, Lance %A Ng, Andrew %A Shah, Nigam H. %B BMC Medical Informatics and Decision Making %V 18 %8 Jan-12-2018 %G eng %N S4 %R 10.1186/s12911-018-0677-8 %0 Journal Article %J Applications in Plant Sciences %D 2018 %T iNaturalist as a tool to expand the research value of museum specimens %A Heberling, J. Mason %A Isaac, Bonnie L. %K AI use cases %B Applications in Plant Sciences %V 6 %P e01193 %8 Jan-11-2018 %G eng %N 11 %R 10.1002/aps3.1193 %0 Book %D 2018 %T Investigating ergonomics in the context of human-robot collaboration as a sociotechnical system %A Rücker, Daniel %A Hornfeck, Rüdiger %A Paetzold, Kristin %E Chen, Jessie %I Springer International Publishing %C Cham %V 784 %P 127 - 135 %@ 978-3-319-94345-9 %G eng %R 10.1007/978-3-319-94346-6_12 %0 Book %D 2018 %T Machine intelligence: Blessing or curse? It depends on us! %A Helbing, Dirk %E Helbing, Dirk %I Springer International Publishing %C Cham %P 25 - 39 %@ 978-3-319-90868-7 %G eng %R 10.1007/978-3-319-90869-4_4 %0 Book %D 2018 %T Machine Learning for Ecology and Sustainable Natural Resource ManagementUse of Machine Learning (ML) for Predicting and Analyzing Ecological and ‘Presence Only’ Data: An Overview of Applications and a Good Outlook %A Huettmann, Falk %A Craig, Erica H. %A Herrick, Keiko A. %A Baltensperger, Andrew P. %A Grant Humphries %A Lieske, David J. %A Miller, Katharine %A Mullet, Timothy C. %A Oppel, Steffen %A Resendiz, Cynthia %A Rutzen, Imme %A Schmid, Moritz S. %A Suwal, Madan K. %A Young, Brian D. %E Grant Humphries %E Magness, Dawn R. %E Huettmann, Falk %I Springer International Publishing %C Cham %P 27 - 61 %@ 978-3-319-96976-3 %G eng %R 10.1007/978-3-319-96978-7_2 %0 Book %D 2018 %T Machine Learning for Ecology and Sustainable Natural Resource ManagementMachine Learning and ‘The Cloud’ for Natural Resource Applications: Autonomous Online Robots Driving Sustainable Conservation Management Worldwide? %A Grant Humphries %A Huettmann, Falk %E Grant Humphries %E Magness, Dawn R. %E Huettmann, Falk %I Springer International Publishing %C Cham %P 353 - 377 %@ 978-3-319-96976-3 %G eng %R 10.1007/978-3-319-96978-7_18 %0 Book Section %B Machine Learning for Ecology and Sustainable Natural Resource Management %D 2018 %T Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Science, Code Sharing, Metadata and a Brief Historical Perspective %A Grant Humphries %A Huettmann, Falk %E Grant Humphries %E Magness, Dawn R. %E Huettmann, Falk %B Machine Learning for Ecology and Sustainable Natural Resource Management %I Springer International Publishing %C Cham %P 3 - 26 %@ 978-3-319-96976-3 %G eng %R 10.1007/978-3-319-96978-7_1 %0 Journal Article %J Jurnal Aplikasi Manajemen %D 2018 %T Managing talented worker in the era of new psychological contract %A Haryadi, Haryadi %A Anggraeni, Ade Irma %A Ibrahim, Daing Nasir %B Jurnal Aplikasi Manajemen %V 16 %P 20 - 26 %8 Jan-03-2018 %G eng %N 1 %R 10.21776/ub.jam.2018.016.01.03 %0 Journal Article %J npj Digital Medicine %D 2018 %T A principled machine learning framework improves accuracy of stage II colorectal cancer prognosis %A Dimitriou, Neofytos %A Arandjelović, Ognjen %A Harrison, David J. %A Caie, Peter D. %B npj Digital Medicine %V 1 %8 Jan-12-2018 %G eng %N 1 %R 10.1038/s41746-018-0057-x %0 Journal Article %J Business & Information Systems Engineering %D 2018 %T Robotic process automation %A van der Aalst, Wil M. P. %A Bichler, Martin %A Heinzl, Armin %B Business & Information Systems Engineering %V 60 %P 269 - 272 %8 Jan-08-2018 %G eng %N 4 %R 10.1007/s12599-018-0542-4 %0 Report %D 2018 %T Skill shift automation and the future of the workforce %A Jaccques Bughin %A Eric Hazan %A Susan Lund %A Peter Dahlstrom %A Anna Wiesinger %A Amresh Subramaniam %B McKinsey Global Institute %8 05/2018 %G eng %U https://www.mckinsey.com/featured-insights/future-of-work/skill-shift-automation-and-the-future-of-the-workforce %0 Journal Article %J The Information Society %D 2018 %T Social robots as cultural objects: The sixth dimension of dynamicity? %A Fortunati, Leopoldina %A Sarrica, Mauro %A Ferrin, Giovanni %A Brondi, Sonia %A Honsell, Furio %B The Information Society %V 34 %P 141 - 152 %8 Mar-05-2020 %G eng %N 3 %R 10.1080/01972243.2018.1444253 %0 Journal Article %J IEEE Intelligent Transportation Systems Magazine %D 2018 %T System architecture of a driverless electric car in the grand cooperative driving challenge %A Xu, Philippe %A Dherbomez, Gerald %A Hery, Elwan %A Abidli, Abderrahmen %A Bonnifait, Philippe %B IEEE Intelligent Transportation Systems Magazine %V 10 %P 47 - 59 %8 Jan-21-2018 %G eng %N 1 %R 10.1109/MITS.2017.2776135 %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 Journal Article %J BMC Health Services Research %D 2018 %T Will artificial intelligence solve the human resource crisis in healthcare? %A Meskó, Bertalan %A Hetényi, Gergely %A Győrffy, Zsuzsanna %B BMC Health Services Research %V 18 %8 Jan-12-2018 %G eng %N 1 %R 10.1186/s12913-018-3359-4 %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 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 Journal Article %J Southeast Asian Economies %D 2017 %T Automation, computerization and future employment in Singapore %A Fuei, Lee King %B Southeast Asian Economies %V 34 %P 388 - 399 %8 Jul-08-2019 %G eng %N 2 %R 10.1355/ae34-2h %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 The Journal of Strategic Information Systems %D 2017 %T Debating big data: A literature review on realizing value from big data %A Günther, Wendy Arianne %A Rezazade Mehrizi, Mohammad H. %A Huysman, Marleen %A Feldberg, Frans %B The Journal of Strategic Information Systems %V 26 %P 191 - 209 %8 Jan-09-2017 %G eng %N 3 %R 10.1016/j.jsis.2017.07.003 %0 Newspaper Article %B Wall Street Journal %D 2017 %T Elon Musk Lays Out Worst-Case Scenario for AI Threat %A Tim Higgins %B Wall Street Journal %G eng %U https://www.wsj.com/articles/elon-musk-warns-nations-governors-of-looming-ai-threat-calls-for-regulations-1500154345 %0 Thesis %B Employment and wage effects %D 2017 %T Evaluating the effects of industrial robots on the European labour market %A Elena Natalie %A Cagnol Hveem %K economics of automation %B Employment and wage effects %V Bergen, Fall, 2017 %G eng %U https://pdfs.semanticscholar.org/4e19/3a0a02315803d799520b094cce36a1888a94.pdf %0 Journal Article %J Health and Technology %D 2017 %T Google DeepMind and healthcare in an age of algorithms %A Powles, Julia %A Hodson, Hal %B Health and Technology %V 7 %P 351 - 367 %8 Jan-12-2017 %G eng %N 4 %R 10.1007/s12553-017-0179-1 %0 Journal Article %J The Journal of Financial Perspectives: Insurance %D 2017 %T Impact of robotics, RPA and AI on the insurance industry %A Chris Lamberton %A Damiano Brigo %A Dave Hoy %B The Journal of Financial Perspectives: Insurance %G eng %U https://www.ey.com/Publication/vwLUAssets/ey-impact-of-robotics-rpa-and-ai-on-the-insurance-industry-challenges-and-opportunities/%24File/ey-impact-of-robotics-rpa-and-ai-on-the-insurance-industry-challenges-and-opportunities.pdf %0 Conference Paper %B Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing %D 2017 %T Putting the Pieces Back Together Again: Contest Webs for Large-Scale Problem Solving %A Malone, Thomas W. %A Jeffrey V Nickerson %A Laubacher, Robert J. %A Fisher, Laur Hesse %A de Boer, Patrick %A Han, Yue %A Towne, W. Ben %K climate change %K collective intelligence %K contest webs %K contests %K Coordination %K incentives %K knowledge reuse %B Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing %I ACM %C New York, NY, USA %P 1661–1674 %@ 978-1-4503-4335-0 %G eng %U http://doi.acm.org/10.1145/2998181.2998343 %R 10.1145/2998181.2998343 %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 International Journal of Robotics Research %D 2017 %T Situational awareness, workload, and workflow preferences %A Gombolay, Matthew %A Bair, Anna %A Huang, Cindy %A Shah, Julie %B The International Journal of Robotics Research %V 36 %P 597 - 617 %8 Sep-06-2017 %G eng %N 5-7 %R 10.1177/0278364916688255 %0 Conference Paper %B SPIE Defense + SecurityMicro- and Nanotechnology Sensors, Systems, and Applications IX %D 2017 %T SPIE ProceedingsBuilding a framework to manage trust in automation %A Metcalfe, J. S. %A Amar R. Marathe %A Haynes, B. %A Paul, V. J. %A Gremillion, G. M. %A Drnec, K. %A Atwater, C. %A Estepp, J. R. %A Lukos, J. R. %A Carter, E. C. %A W. D. Nothwang %E George, Thomas %E Dutta, Achyut K. %E Islam, M. Saif %B SPIE Defense + SecurityMicro- and Nanotechnology Sensors, Systems, and Applications IX %I SPIE %C Anaheim, California, United States %V 10194 %P 101941U %G eng %R 10.1117/12.2264245 %0 Case %D 2016 %T Regional Diversity in Autonomy and Work: A Case Study from Uber and Lyft Drivers %A Alex Rosenblat %A Tim Hwang %B Data And Society %G eng %U https://datasociety.net/pubs/ia/Rosenblat-Hwang_Regional_Diversity-10-13.pdf %& Data And Society %0 Journal Article %J Computers in Human Behavior %D 2015 %T A comparison between human–human online conversations and human–chatbot conversations %A Hill, Jennifer %A Randolph Ford, W. %A Farreras, Ingrid G. %B Computers in Human Behavior %V 49 %P 245 - 250 %8 Jan-08-2015 %G eng %R 10.1016/j.chb.2015.02.026 %0 Magazine Article %D 2015 %T Facebook launches M, its bold answer to Siri and Cortana %A Jessi Hempel %G eng %U https://www.wired.com/2015/08/facebook-launches-m-new-kind-virtual-assistant/ %0 Conference Paper %B Proceedings of the 2013 conference on Computer supported cooperative work %D 2013 %T The future of crowd work %A Kittur, Aniket %A Jeffrey V Nickerson %A Michael S. Bernstein %A Gerber, Elizabeth %A Shaw, Aaron %A Zimmerman, John %A Lease, Matt %A Horton, John %B Proceedings of the 2013 conference on Computer supported cooperative work %I ACM %P 1301–1318 %G eng %0 Journal Article %J Research Policy %D 2013 %T The rise and fall of interdisciplinary research: The case of open source innovation %A Raasch, Christina %A Lee, Viktor %A Spaeth, Sebastian %A Herstatt, Cornelius %B Research Policy %V 42 %P 1138 - 1151 %8 Jan-06-2013 %G eng %N 5 %R 10.1016/j.respol.2013.01.010 %0 Journal Article %J Journal of Organizational Behavior %D 2010 %T Not what it was and not what it will be: The future of job design research %A Oldham, Greg R. %A Hackman, J. Richard %E Grant, Adam %E Fried, Yitzhak %E Parker, Sharon %E Frese, Michael %B Journal of Organizational Behavior %V 31 %P 463 - 479 %8 Jan-02-2010 %G eng %N 2-3 %R 10.1002/job.v31:2/310.1002/job.678 %0 Journal Article %J Journal of Organizational Behavior %D 2010 %T Understanding the role of occupational and organizational context %A Morgeson, Frederick P. %A Dierdorff, Erich C. %A Hmurovic, Jillian L. %E Grant, Adam %E Fried, Yitzhak %E Parker, Sharon %E Frese, Michael %B Journal of Organizational Behavior %V 31 %P 351 - 360 %8 Jan-02-2010 %G eng %N 2-3 %R 10.1002/job.642 %0 Conference Paper %B the 27th international conference extended abstractsProceedings of the 27th international conference extended abstracts on Human factors in computing systems - CHI EA '09 %D 2009 %T The changing face of digital science %A Aragon, Cecilia R. %A Poon, Sarah %A Silva, Claudio T. %Y Olsen, Dan R. %Y Arthur, Richard B. %Y Hinckley, Ken %Y Morris, Meredith Ringel %Y Hudson, Scott %Y Greenberg, Saul %B the 27th international conference extended abstractsProceedings of the 27th international conference extended abstracts on Human factors in computing systems - CHI EA '09 %I ACM Press %C Boston, MA, USANew York, New York, USA %P 4819 %@ 9781605582474 %G eng %R 10.1145/1520340.1520749 %0 Book %D 2008 %T Handbook of transdisciplinary research %E Hadorn, Gertrude Hirsch %E Hoffmann-Riem, Holger %E Biber-Klemm, Susette %E Grossenbacher-Mansuy, Walter %E Joye, Dominique %E Pohl, Christian %I Springer Netherlands %C Dordrecht %P 427 - 432 %@ 978-1-4020-6698-6 %G eng %R 10.1007/978-1-4020-6699-3_28 %0 Journal Article %J Research in Personnel and Human Resources ManagementResearch in Personnel and Human Resources Management %D 2008 %T Job and team design: Toward a more integrative conceptualization of work design %A Frederick P. 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