@article {9999, title = {Technological Frames: Interpretations about the Futures of Work and Intelligent Machines on Social Media}, journal = {Information, Technology \& People}, year = {In Press}, abstract = {Purpose: This study explores interpretations and feelings about futures of work and intelligent machines expressed on social media. Design/methodology/approach: We investigate public interpretations, assumptions and expectations expressed in social media conversations through which users freely share their most recent ideas. In addition to frames, this study also coded the emotions and attitudes expressed in the text data. More specifically, a corpus consisting of 998 unique Reddit post titles and their corresponding 16,611 comments were analyzed by using computer-aided textual analysis comprising a BERTopic model, and two BERT text classification models, one for emotion and the other for sentiment analysis, supported by human judgment. Finally, relationships among frames and attitudes and frames and emotions were examined. Findings: Twelve clusters were found that related to futures of work with intelligent machines. Based on the prior literature, two frames were chosen from these clusters and analyzed in detail: (1) general impacts of intelligent machines on wealth and society and (2) replacement of tasks (augmentation and substitution). The general attitude observed in conversations was positive, moreover the most common emotion category was approval. Findings also showed there are relationships between frames and attitudes and frames and emotions. Originality: This work extends the prior literature on a topic relevant for academia and industry. Findings of this research can help realize potential needs and benefits from the public{\textquoteright}s vantage point in the case of possible transformations in the future of work with intelligent machines. The findings may also help enlighten researchers to shape research directions about futures of work. Furthermore, firms, organizations or industries may also employ framing methods to receive customers{\textquoteright} or workers{\textquoteright} responses, or even to influence the responses. Aside from the empirical findings, another crucial implication of this work is application of theory of technological frames for systematizing the interpretations of how people conceptualize the future of work with the technology of intelligent machines. This study constitutes a bridge that connects fields of IS, computational science and empirical social research. }, attachments = {https://waim.network/sites/crowston.syr.edu/files/frames\%20to\%20share.pdf}, author = {Ay{\c s}e {\"O}cal and Kevin Crowston} } @article {9998, title = {Making sense of AI systems development}, journal = {IEEE Transactions on Software Engineering}, volume = {50}, year = {2024}, month = {12/2023}, pages = {123{\textendash}140}, abstract = {We identify and describe episodes of sensemaking around challenges in modern Artificial-Intelligence (AI)-based systems development that emerged in projects carried out by IBM and client companies. All projects used IBM Watson as the development platform for building tailored AI-based solutions to support workers or customers of the client companies. Yet, many of the projects turned out to be significantly more challenging than IBM and its clients had expected. The analysis reveals that project members struggled to establish reliable meanings about the technology, the project, context, and data to act upon. The project members report multiple aspects of the projects that they were not expecting to need to make sense of yet were problematic. Many issues bear upon the current-generation AI{\textquoteright}s inherent characteristics, such as dependency on large data sets and continuous improvement as more data becomes available. Those characteristics increase the complexity of the projects and call for balanced mindfulness to avoid unexpected problems.}, doi = {10.1109/TSE.2023.3338857}, attachments = {https://waim.network/sites/crowston.syr.edu/files/sensemaking_tse_to_share.pdf}, author = {Mateusz Dolata and Kevin Crowston} } @proceedings {wang2024reelframer, title = {ReelFramer: Human-AI Co-Creation for News-to-Video Translation}, year = {2024}, address = {Honolulu, Hawai{\textquoteright}i}, abstract = {

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.

}, url = {https://arxiv.org/abs/2304.09653}, author = {Sitong Wang and Samia Menon and Tao Long and Keren Henderson and Dingzeyu Li and Kevin Crowston and Mark Hansen and Jeffrey V. Nickerson and Lydia B. Chilton} } @proceedings {petridis2023anglekindling, title = {AngleKindling: Supporting Journalistic Angle Ideation with Large Language Models}, year = {2023}, doi = {10.1145/3544548.3580907}, url = {https://savvaspetridis.github.io/papers/anglekindling.pdf}, author = {Petridis, Savvas and Diakopoulos, Nicholas and Crowston, Kevin and Hansen, Mark and Henderson, Keren and Jastrzebski, Stan and Nickerson, Jefrey V and Chilton, Lydia B} } @article {9998, title = {Artificial intelligence in information systems: State of the art and research roadmap}, journal = {Communications of the Association for Information Systems (CAIS)}, volume = {50}, year = {2022}, abstract = {

Many would argue that artificial intelligence (AI) is not just a technology but represents a paradigmatic shift in the relationship between humans and machines. Much of the literature assumes that AI-powered practices are substantially different and profoundly changes organizational structures, communication, affordances, and ecosystems. However, research in AI is often fragmented and lacks clarity. While the Information Systems (IS) field can play a pivotal role in the emergence and use of AI, there is a need for a clear direction that specifies how IS can contribute and what are to be the key research themes and questions. This paper draws on a PDW at ICIS 2020 and the discussions that followed. It summarizes and synthesizes five decades of the impact of AI on organizational practices, providing views from various perspectives. It identifies weaknesses in the current AI literature as measured against conceptual clarity, theoretical glue, cumulative tradition, parsimony, and applicability. The paper concludes by identifying direct actions that the IS research community can undertake to address these issues. The final contribution is a next-step research agenda to guide AI research in the coming years.

}, doi = {10.17705/1CAIS.05017}, attachments = {https://waim.network/sites/crowston.syr.edu/files/Artificial\%20Intelligence\%20in\%20Information\%20Systems\%20State\%20of\%20the\%20Art.pdf}, author = {P{\"a}r J. {\r A}gerfalk and Kieran Conboy and Kevin Crowston and Sirkka L. Jarvenpaa and Jenny Eriksson Lundstr{\"o}m and Patrick Mikalef and Sudha Ram} } @conference {9999, title = {Project archetypes: A blessing and a curse for AI development}, booktitle = {International Conference on Information Systems (ICIS)}, year = {2022}, address = {Copenhagen, Denmark}, abstract = {

Software projects rely on what we propose to call project archetypes, i.e., pre-existing mental images of how projects work. They guide distribution of responsibilities, planning, or expectations. However, with the technological progress, project archetypes may become outdated, ineffective, or counterproductive by impeding more adequate approaches. Understanding archetypes of software development projects is core to leverage their potential. The development of applications using machine learning and artificial intelligence provides a context in which existing archetypes might outdate and need to be questioned, adapted, or replaced. We analyzed 36 interviews from 21 projects between IBM Watson and client companies and identified four project archetypes members initially used to understand the projects. We then derive a new project archetype, cognitive computing project, from the interviews. It can inform future development projects based on AI-development platforms. Project leaders should pro-actively manage project archetypes while researchers should investigate what guides initial understandings of software projects.

}, url = {https://aisel.aisnet.org/icis2022/is_design/is_design/6 }, attachments = {https://waim.network/sites/crowston.syr.edu/files/icis_archetypes_rev1_v20_zora.pdf}, author = {Mateusz Dolata and Kevin Crowston and Gerhard Schwabe} } @article {2021, title = {Hybrid intelligence in business networks}, journal = {Electronic Markets}, year = {2021}, month = {Nov-06-2021}, issn = {1019-6781}, doi = {10.1007/s12525-021-00481-4}, attachments = {https://waim.network/sites/crowston.syr.edu/files/Ebel2021_Article_HybridIntelligenceInBusinessNe.pdf}, author = {Ebel, Philipp and S{\"o}llner, Matthias and Leimeister, Jan Marco and Crowston, Kevin and de Vreede, Gert-Jan} } @conference {2020, title = {Algorithmic Journalism and Its Impacts on Work}, booktitle = {Computation + Journalism Symposium }, year = {2020}, edition = {(cancelled due to COVID; presented in 2021)}, abstract = {

In the artificial intelligence era, algorithmic journalists can produce news reports in natural language from structured data thanks to natural language generation (NLG) algorithms. This paper presents several algorithmic content generation models and discusses the impacts of algorithmic journalism on work within a framework consisting of three levels: replacing tasks of journalists, increasing efficiency, and developing new capabilities within journalism. The findings indicate that algorithmic journalism technology may lead some changes in journalism by enabling individual users to produce their own stories. This paper may contribute to an understanding of how algorithmic news is created and how algorithmic journalism technology impacts work.

}, url = {https://cpb-us-w2.wpmucdn.com/express.northeastern.edu/dist/d/53/files/2020/02/CJ_2020_paper_26.pdf}, attachments = {https://waim.network/sites/crowston.syr.edu/files/CJ_2020_paper_26.pdf}, author = {Ayse Dalgali and Kevin Crowston} } @article {2020, title = {Algorithms at work: The new contested terrain of control}, journal = {Academy of Management Annals}, volume = {14}, year = {2020}, month = {Jan-01-2020}, pages = {366 - 410}, issn = {1941-6520}, doi = {10.5465/annals.2018.0174}, author = {Kellogg, Katherine C. and Valentine, Melissa A. and Christin, Ang{\'e}le} } @article {Gentili2020, title = {Are machines stealing our jobs?}, journal = {Cambridge Journal of Regions, Economy and Society}, volume = {13}, number = {1}, year = {2020}, pages = {153{\textendash}173}, abstract = {This study aims to contribute empirical evidence to the debate about the future of work in an increasingly robotised world. We implement a data-driven approach to study the technological transition in six leading Organisation for Economic Co-operation and Development (OECD) countries. First, we perform a cross-country and cross-sector cluster analysis based on the OECD-STAN database. Second, using the International Federation of Robotics database, we bridge these results with those regarding the sectoral density of robots. We show that the process of robotisation is industry- and country-sensitive. In the future, participants in the political and academic debate may be split into optimists and pessimists regarding the future of human labour; however, the two stances may not be contradictory.}, keywords = {cluster analysis, e24, e66, j24, jel classifications, labour dislocation, robotisation}, issn = {1752-1378}, doi = {10.1093/cjres/rsz025}, author = {Gentili, Andrea and Compagnucci, Fabiano and Gallegati, Mauro and Valentini, Enzo} } @book {2020, title = {Artificial Intelligence and Judicial Modernization}, year = {2020}, publisher = {Springer Singapore}, organization = {Springer Singapore}, address = {Singapore}, isbn = {978-981-32-9879-8}, doi = {10.1007/978-981-32-9880-4}, author = {Cui, Yadong} } @booklet {Cebreros2020, title = {Automation Technologies and Employment at Risk : The Case of Mexico}, year = {2020}, publisher = {Banco de M{\'e}xico}, author = {Cebreros, Alfonso and Heffner-Rodr{\'\i}guez, Aldo and Livas, Ren{\'e} and Puggioni, Daniela} } @article {Roccetti2020, title = {A Cautionary Tale for Machine Learning Design: why we Still Need Human-Assisted Big Data Analysis}, journal = {Mobile Networks and Applications}, volume = {25}, number = {3}, year = {2020}, pages = {1075{\textendash}1083}, publisher = {Mobile Networks and Applications}, abstract = {Supervised Machine Learning (ML) requires that smart algorithms scrutinize a very large number of labeled samples before they can make right predictions. And this is not always true either. In our experience, in fact, a neural network trained with a huge database comprised of over fifteen million water meter readings had essentially failed to predict when a meter would malfunction/need disassembly based on a history of water consumption measurements. With a second step, we developed a methodology, based on the enforcement of a specialized data semantics, that allowed us to extract only those samples for training that were not noised by data impurities. With this methodology, we re-trained the neural network up to a prediction accuracy of over 80\%. Yet, we simultaneously realized that the new training dataset was significantly different from the initial one in statistical terms, and much smaller, as well. We had reached a sort of paradox: We had alleviated the initial problem with a better interpretable model, but we had changed the replicated form of the initial data. To reconcile that paradox, we further enhanced our data semantics with the contribution of field experts. This has finally led to the extrapolation of a training dataset truly representative of regular/defective water meters and able to describe the underlying statistical phenomenon, while still providing an excellent prediction accuracy of the resulting classifier. At the end of this path, the lesson we have learnt is that a human-in-the-loop approach may significantly help to clean and re-organize noised datasets for an empowered ML design experience.}, keywords = {Human-in-the-loop methods, Human-machine-Bigdata interaction loop, Machine learning design, Smart data, Water metering and consumption}, issn = {15728153}, doi = {10.1007/s11036-020-01530-6}, author = {Roccetti, Marco and Delnevo, Giovanni and Casini, Luca and Salomoni, Paola} } @proceedings {2020, title = {Factors Influencing Approval of Wikipedia Bots}, year = {2020}, address = {Wailea, HI}, abstract = {

Before a Wikipedia bot is allowed to edit, the operator of the bot must get approval. The Bot Approvals Group (BAG), a committee of Wikipedia bot developers, users and editors, discusses each bot request to reach consensus regarding approval or denial. We examine factors related to approval of a bot by analyzing 100 bots{\textquoteright} project pages. The results suggest that usefulness, value-based decision making and the bot{\textquoteright}s status (e.g., automatic or manual) are related to approval. This study may contribute to understanding decision making regarding the human-automation boundary and may lead to developing more efficient bots.

}, doi = {10.24251/HICSS.2020.018}, attachments = {https://waim.network/sites/crowston.syr.edu/files/HICSS_WikipediaPaper_3.9.new\%20kc\%20\%282\%29.pdf}, author = {Ayse Dalgali and Kevin Crowston} } @article {Reid-musson2020, title = {Feminist economic geography and the future of work}, journal = {EPA: Economy and Space}, year = {2020}, pages = {1{\textendash}12}, keywords = {corresponding author, department of geography, emily reid-musson, feminist economic geography, memorial university of newfoundland, newfoundland and labrador a1c, social reproduction, subjectivity, technology, work}, doi = {10.1177/0308518X20947101}, author = {Reid-musson, Emily and Cockayne, Daniel and Frederiksen, Lia and Worth, Nancy} } @proceedings {2018, title = {The Genie in the Bottle: Different Stakeholders, Different Interpretations of Machine Learning}, year = {2020}, type = {Working paper}, address = {Wailea, HI}, abstract = {

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.

}, doi = {10.24251/HICSS.2020.719 }, attachments = {https://waim.network/sites/crowston.syr.edu/files/Social_Construction_of_ML_in_GS_HICCS2020.pdf}, author = {Mahboobeh Harandi and Kevin Crowston and Corey Jackson and Carsten {\O}sterlund} } @proceedings {9999, title = {Impacts of the Use of Machine Learning on Work Design}, year = {2020}, month = {11/2020}, publisher = {ACM}, address = {Virtual Event, NSW, Australia}, abstract = {

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

}, keywords = {artificial intelligence, automation, Coordination, machine learning, work design}, isbn = {978-1-4503-8054-6/20/11}, doi = {10.1145/3406499.3415070}, attachments = {https://waim.network/sites/crowston.syr.edu/files/Impacts_of_ML_for_HAI_2020.pdf}, author = {Kevin Crowston and Bolici, Francesco} } @conference {Das2020, title = {Learning occupational task-shares dynamics for the future of work}, booktitle = {AIES 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society}, year = {2020}, pages = {36{\textendash}42}, abstract = {The recent wave of AI and automation has been argued to differ from previous General Purpose Technologies (GPTs), in that it may lead to rapid change in occupations{\textquoteright} underlying task requirements and persistent technological unemployment. In this paper, we apply a novel methodology of dynamic task shares to a large dataset of online job postings to explore how exactly occupational task demands have changed over the past decade of AI innovation, especially across high, mid and low wage occupations. Notably, big data and AI have risen significantly among high wage occupations since 2012 and 2016, respectively. We built an ARIMA model to predict future occupational task demands and showcase several relevant examples in Healthcare, Administration, and IT. Such task demands predictions across occupations will play a pivotal role in retraining the workforce of the future.}, keywords = {AI, automation, future of work, Occupational Task Demands}, isbn = {9781450371100}, doi = {10.1145/3375627.3375826}, author = {Das, Subhro and Steffen, Sebastian and Clarke, Wyatt and Reddy, Prabhat and Brynjolfsson, Erik and Fleming, Martin} } @conference {2020, title = {Lessons for Supporting Data Science from the Everyday Automation Experience of Spell-Checkers}, booktitle = {Automation Experience across Domains (AutomationXP20), CHI{\textquoteright}20 Workshop, 26 April 2020, Virtual}, year = {2020}, month = {4/2020}, address = {Virtual workshop}, abstract = {

We apply two theoretical frameworks to analyze spell-checkers as a form of automation and apply the lessons learned to analyze opportunities to support data science. The analysis distinguishes between automation of analysis to suggest actions\  and automation of implementation of actions. Having the automation work in the same space as users (e.g., editing the same document) supports stigmergic coordination between the two, but attention is needed to ensure that the contributions can be combined and have a recognizable form that indicates their purpose.

}, attachments = {https://waim.network/sites/crowston.syr.edu/files/Everyday_automation\%20camera\%20ready.pdf}, author = {Kevin Crowston} } @article {Capatina2020, title = {Matching the future capabilities of an artificial intelligence-based software for social media marketing with potential users{\textquoteright} expectations}, journal = {Technological Forecasting and Social Change}, volume = {151}, number = {June 2019}, year = {2020}, pages = {119794}, publisher = {Elsevier}, abstract = {The increasing use of Artificial Intelligence (AI) in Social Media Marketing (SMM) triggered the need for this research to identify and further analyze such expectations of potential users of an AI-based software for Social Media Marketing; a software that will be developed in the next two years, based on its future capabilities. In this research, we seek to discover how the potential users of this AI-based software (owners and employees from digital agencies based in France, Italy and Romania, as well as freelancers from these countries, with expertise in SMM) perceive the capabilities that we offer, as a way to differentiate our technological solution from other available in the market. We propose a causal model to find out which expected capabilities of the future AI-based software can explain potential users{\textquoteright} intention to test and use this innovative technological solution for SMM, based on integer valued regression models. With this purpose, R software is used to analyze the data provided by the respondents. We identify different causal configurations of upcoming capabilities of the AI-based software, classified in three categories (audience, image and sentiment analysis), and will trigger potential users{\textquoteright} intention to test and use the software, based on an fsQCA approach.}, keywords = {artificial intelligence, Audience analysis, Image analysis, machine learning, Sentiment analysis, Social media marketing}, issn = {00401625}, doi = {10.1016/j.techfore.2019.119794}, url = {https://doi.org/10.1016/j.techfore.2019.119794}, author = {Capatina, Alexandru and Kachour, Maher and Lichy, Jessica and Micu, Adrian and Micu, Angela Eliza and Codignola, Federica} } @article {Petersen2020, title = {The Role of Discretion in the Age of Automation}, journal = {Computer Supported Cooperative Work: CSCW: An International Journal}, volume = {29}, number = {3}, year = {2020}, pages = {303{\textendash}333}, abstract = {This paper examines the nature of discretion in social work in order to debunk myths dominating prevalent debates on digitisation and automation in the public sector. Social workers have traditionally used their discretion widely and with great autonomy, but discretion has increasingly come under pressure for its apparent subjectivity and randomness. In Denmark, our case in point, the government recently planned to standardise laws to limit or remove discretion where possible in order for automation of case management to gain a foothold. Recent studies have focused on discretion in the public sector, but few have examined it explicitly and as part of real cases. As a consequence, they often leave the myths about discretion unchallenged. Inspired by the literature on discretion and CSCW research on rules in action, this study reports on an empirical investigation of discretion in child protection services in Denmark. The results of our analysis provide a new understanding of discretion as a cooperative endeavour, based on consultation and skill, rather than an arbitrary or idiosyncratic choice. In this manner, our study contradicts the myth of discretion inherent in the automation agenda. Correspondingly, we ask for attention to be given to systems that integrate discretion with technology rather than seek to undermine it directly or get around it surreptitiously. In this age of automation, this is not only an important but also an urgent task for CSCW researchers to fulfil.}, keywords = {Administrative work, automation, Casework, Decision-Making, Digital-ready legislation, Digitisation, Discretion, Rules in action, Social work}, issn = {15737551}, doi = {10.1007/s10606-020-09371-3}, author = {Petersen, Anette C.M. and Christensen, Lars Rune and Hildebrandt, Thomas T.} } @article {Brundage2020, title = {Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims}, year = {2020}, pages = {1{\textendash}9}, abstract = {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{\textendash}spanning institutions, software, and hardware{\textendash}and make recommendations aimed at implementing, exploring, or improving those mechanisms.}, url = {http://arxiv.org/abs/2004.07213}, author = {Brundage, Miles and Avin, Shahar and Wang, Jasmine and Belfield, Haydn and Krueger, Gretchen and Hadfield, Gillian and Khlaaf, Heidy and Yang, Jingying and Toner, Helen and Fong, Ruth and Maharaj, Tegan and Koh, Pang Wei and Hooker, Sara and Leung, Jade and Trask, Andrew and Bluemke, Emma and Lebensold, Jonathan and O{\textquoteright}Keefe, Cullen and Koren, Mark and Ryffel, Th{\'e}o and Rubinovitz, JB and Besiroglu, Tamay and Carugati, Federica and Clark, Jack and Eckersley, Peter and de Haas, Sarah and Johnson, Maritza and Laurie, Ben and Ingerman, Alex and Krawczuk, Igor and Askell, Amanda and Cammarota, Rosario and Lohn, Andrew and Krueger, David and Stix, Charlotte and Henderson, Peter and Graham, Logan and Prunkl, Carina and Martin, Bianca and Seger, Elizabeth and Zilberman, Noa and H{\'E}igeartaigh, Se{\'a}n {\'O} and Kroeger, Frens and Sastry, Girish and Kagan, Rebecca and Weller, Adrian and Tse, Brian and Barnes, Elizabeth and Dafoe, Allan and Scharre, Paul and Herbert-Voss, Ariel and Rasser, Martijn and Sodhani, Shagun and Flynn, Carrick and Gilbert, Thomas Krendl and Dyer, Lisa and Khan, Saif and Bengio, Yoshua and Anderljung, Markus} } @article {Clifton2020, title = {When machines think for us: The consequences for work and place}, journal = {Cambridge Journal of Regions, Economy and Society}, volume = {13}, number = {1}, year = {2020}, pages = {3{\textendash}23}, abstract = {The relationship between technology and work, and concerns about the displacement effects of technology and the organisation of work, have a long history. The last decade has seen the proliferation of academic papers, consultancy reports and news articles about the possible effects of Artificial Intelligence (AI) on work-creating visions of both utopian and dystopian workplace futures. AI has the potential to transform the demand for labour, the nature of work and operational infrastructure by solving complex problems with high efficiency and speed. However, despite hundreds of reports and studies, AI remains an enigma, a newly emerging technology, and its rate of adoption and implications for the structure of work are still only beginning to be understood. The current anxiety about labour displacement anticipates the growth and direct use of AI. Yet, in many ways, at present AI is likely being overestimated in terms of impact. Still, an increasing body of research argues the consequences for work will be highly uneven and depend on a range of factors, including place, economic activity, business culture, education levels and gender, among others. We appraise the history and the blurry boundaries around the definitions of AI. We explore the debates around the extent of job augmentation, substitution, destruction and displacement by examining the empirical basis of claims, rather than mere projections. Explorations of corporate reactions to the prospects of AI penetration, and the role of consultancies in prodding firms to embrace the technology, represent another perspective onto our inquiry. We conclude by exploring the impacts of AI changes in the quantity and quality of labour on a range of social, geographic and governmental outcomes.}, keywords = {artificial intelligence, automation, bias in machine learning, geography of technology, job displacement and growth}, issn = {17521386}, doi = {10.1093/cjres/rsaa004}, author = {Clifton, Judith and Clifton, Judith and Glasmeier, Amy and Gray, Mia} } @booklet {Commission2020, title = {White Paper on Artificial Intelligence - A European approach to excellence and trust}, howpublished = {COM(2020) 65 final}, year = {2020}, abstract = {Predicting the binding mode of flexible polypeptides to proteins is an important task that falls outside the domain of applicability of most small molecule and protein-protein docking tools. Here, we test the small molecule flexible ligand docking program Glide on a set of 19 non-α-helical peptides and systematically improve pose prediction accuracy by enhancing Glide sampling for flexible polypeptides. In addition, scoring of the poses was improved by post-processing with physics-based implicit solvent MM- GBSA calculations. Using the best RMSD among the top 10 scoring poses as a metric, the success rate (RMSD <= 2.0 {\r A} for the interface backbone atoms) increased from 21\% with default Glide SP settings to 58\% with the enhanced peptide sampling and scoring protocol in the case of redocking to the native protein structure. This approaches the accuracy of the recently developed Rosetta FlexPepDock method (63\% success for these 19 peptides) while being over 100 times faster. Cross-docking was performed for a subset of cases where an unbound receptor structure was available, and in that case, 40\% of peptides were docked successfully. We analyze the results and find that the optimized polypeptide protocol is most accurate for extended peptides of limited size and number of formal charges, defining a domain of applicability for this approach.}, url = {https://www.cambridge.org/core/product/identifier/CBO9781107415324A009/type/book_part}, author = {Commission, European} } @article {2019, title = {Algorithms at War: The Promise, Peril, and Limits of Artificial Intelligence}, journal = {International Studies Review}, year = {2019}, month = {Dec-06-2020}, issn = {1521-9488}, doi = {10.1093/isr/viz025}, author = {Jensen, Benjamin M and Whyte, Christopher and Cuomo, Scott} } @article {2019, title = {Applications of artificial intelligence to imaging and diagnosis}, journal = {Biophysical Reviews}, volume = {11}, year = {2019}, month = {Jan-02-2019}, pages = {111 - 118}, issn = {1867-2450}, doi = {10.1007/s12551-018-0449-9}, author = {Nichols, James A. and Herbert Chan, Hsien W. and Baker, Matthew A. B.} } @article {2019, title = {Artificial Intelligence and the Future of the Drug Safety Professional}, journal = {Drug Safety}, volume = {42}, year = {2019}, month = {Jan-04-2019}, pages = {491 - 497}, issn = {0114-5916}, doi = {10.1007/s40264-018-0746-z}, author = {Danysz, Karolina and Cicirello, Salvatore and Mingle, Edward and Assuncao, Bruno and Tetarenko, Niki and Mockute, Ruta and Abatemarco, Danielle and Widdowson, Mark and Desai, Sameen} } @article {2019, title = {Artificial intelligence conquers starcraft II in {\textquoteright}unimaginably unusual{\textquoteright} AI breakthrough}, journal = {Independent}, year = {2019}, url = {https://www.independent.co.uk/life-style/gadgets-and-tech/gaming/artificial-intelligence-starcraft-2-ai-deepmind-a9176601.html}, author = {Anthony Cuthbertson} } @article {2019, title = {Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers}, journal = {Industrial Management \& Data Systems}, volume = {119}, year = {2019}, month = {Dec-08-2019}, pages = {1411 - 1430}, issn = {0263-5577}, doi = {10.1108/IMDS-08-2018-0368}, author = {Belanche, Daniel and Casal{\'o}, Luis V. and Flavi{\'a}n, Carlos} } @conference {2019, title = {Beyond Dyadic Interactions: Considering Chatbots as Community Members}, booktitle = {Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI {\textquoteright}19}, year = {2019}, pages = {1-13}, publisher = {ACM Press}, organization = {ACM Press}, address = {Glasgow, Scotland UK}, isbn = {9781450359702}, doi = {10.1145/3290605}, author = {Seering, Joseph and Luria, Michal and Kaufman, Geoff and Hammer, Jessica} } @article {2019, title = {A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task}, journal = {European Journal of Cancer}, volume = {111}, year = {2019}, month = {Jan-04-2019}, pages = {148 - 154}, issn = {09598049}, doi = {10.1016/j.ejca.2019.02.005}, author = {Brinker, Titus J. and Hekler, Achim and Alexander Enk and Klode, Joachim and Hauschild, Axel and Berking, Carola and Schilling, Bastian and Haferkamp, Sebastian and Schadendorf, Dirk and Fr{\"o}hling, Stefan and Jochen Sven Utikal and von Kalle, Christof and Ludwig-Peitsch, Wiebke and Sirokay, Judith and Heinzerling, Lucie and Albrecht, Magarete and Baratella, Katharina and Bischof, Lena and Chorti, Eleftheria and Dith, Anna and Drusio, Christina and Giese, Nina and Gratsias, Emmanouil and Griewank, Klaus and Hallasch, Sandra and Hanhart, Zdenka and Herz, Saskia and Hohaus, Katja and Jansen, Philipp and Jockenh{\"o}fer, Finja and Kanaki, Theodora and Knispel, Sarah and Leonhard, Katja and Martaki, Anna and Matei, Liliana and Matull, Johanna and Olischewski, Alexandra and Petri, Maximilian and Placke, Jan-Malte and Raub, Simon and Salva, Katrin and Schlott, Swantje and Sody, Elsa and Steingrube, Nadine and Stoffels, Ingo and Ugurel, Selma and Sondermann, Wiebke and Zaremba, Anne and Gebhardt, Christoffer and Booken, Nina and Christolouka, Maria and Buder-Bakhaya, Kristina and Bokor-Billmann, Therezia and Alexander Enk and Gholam, Patrick and H{\"a}n{\ss}le, Holger and Salzmann, Martin and Sch{\"a}fer, Sarah and Sch{\"a}kel, Knut and Schank, Timo and Bohne, Ann-Sophie and Deffaa, Sophia and Drerup, Katharina and Egberts, Friederike and Erkens, Anna-Sophie and Ewald, Benjamin and Falkvoll, Sandra and Gerdes, Sascha and Harde, Viola and Hauschild, Axel and Jost, Marion and Kosova, Katja and Messinger, Laetitia and Metzner, Malte and Morrison, Kirsten and Motamedi, Rogina and Pinczker, Anja and Rosenthal, Anne and Scheller, Natalie and Schwarz, Thomas and St{\"o}lzl, Dora and Thielking, Federieke and Tomaschewski, Elena and Wehkamp, Ulrike and Weichenthal, Michael and Wiedow, Oliver and B{\"a}r, Claudia Maria and Bender-S{\"a}belkampf, Sophia and Horbr{\"u}gger, Marc and Karoglan, Ante and Kraas, Luise and Faulhaber, J{\"o}rg and Geraud, Cyrill and Guo, Ze and Koch, Philipp and Linke, Miriam and Maurier, Nolwenn and M{\"u}ller, Verena and Thomas, Benjamin and Jochen Sven Utikal and Alamri, Ali Saeed M. and Baczako, Andrea and Berking, Carola and Betke, Matthias and Haas, Carolin and Hartmann, Daniela and Heppt, Markus V. and Kilian, Katharina and Krammer, Sebastian and Lapczynski, Natalie Lidia and Mastnik, Sebastian and Nasifoglu, Suzan and Ruini, Cristel and Sattler, Elke and Schlaak, Max and Wolff, Hans and Achatz, Birgit and Bergbreiter, Astrid and Drexler, Konstantin and Ettinger, Monika and Haferkamp, Sebastian and Halupczok, Anna and Hegemann, Marie and Dinauer, Verena and Maagk, Maria and Mickler, Marion and Philipp, Biance and Wilm, Anna and Wittmann, Constanze and Gesierich, Anja and Glutsch, Valerie and Kahlert, Katrin and Kerstan, Andreas and Schilling, Bastian and Schr{\"u}fer, Philipp} } @article {2019, title = {Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task}, journal = {European Journal of Cancer}, volume = {113}, year = {2019}, month = {Jan-05-2019}, pages = {47 - 54}, issn = {09598049}, doi = {10.1016/j.ejca.2019.04.001}, author = {Brinker, Titus J. and Hekler, Achim and Alexander Enk and Klode, Joachim and Hauschild, Axel and Berking, Carola and Schilling, Bastian and Haferkamp, Sebastian and Schadendorf, Dirk and Holland-Letz, Tim and Jochen Sven Utikal and von Kalle, Christof and Ludwig-Peitsch, Wiebke and Sirokay, Judith and Heinzerling, Lucie and Albrecht, Magarete and Baratella, Katharina and Bischof, Lena and Chorti, Eleftheria and Dith, Anna and Drusio, Christina and Giese, Nina and Gratsias, Emmanouil and Griewank, Klaus and Hallasch, Sandra and Hanhart, Zdenka and Herz, Saskia and Hohaus, Katja and Jansen, Philipp and Jockenh{\"o}fer, Finja and Kanaki, Theodora and Knispel, Sarah and Leonhard, Katja and Martaki, Anna and Matei, Liliana and Matull, Johanna and Olischewski, Alexandra and Petri, Maximilian and Placke, Jan-Malte and Raub, Simon and Salva, Katrin and Schlott, Swantje and Sody, Elsa and Steingrube, Nadine and Stoffels, Ingo and Ugurel, Selma and Zaremba, Anne and Gebhardt, Christoffer and Booken, Nina and Christolouka, Maria and Buder-Bakhaya, Kristina and Bokor-Billmann, Therezia and Alexander Enk and Gholam, Patrick and H{\"a}n{\ss}le, Holger and Salzmann, Martin and Sch{\"a}fer, Sarah and Sch{\"a}kel, Knut and Schank, Timo and Bohne, Ann-Sophie and Deffaa, Sophia and Drerup, Katharina and Egberts, Friederike and Erkens, Anna-Sophie and Ewald, Benjamin and Falkvoll, Sandra and Gerdes, Sascha and Harde, Viola and Hauschild, Axel and Jost, Marion and Kosova, Katja and Messinger, Laetitia and Metzner, Malte and Morrison, Kirsten and Motamedi, Rogina and Pinczker, Anja and Rosenthal, Anne and Scheller, Natalie and Schwarz, Thomas and St{\"o}lzl, Dora and Thielking, Federieke and Tomaschewski, Elena and Wehkamp, Ulrike and Weichenthal, Michael and Wiedow, Oliver and B{\"a}r, Claudia Maria and Bender-S{\"a}belkampf, Sophia and Horbr{\"u}gger, Marc and Karoglan, Ante and Kraas, Luise and Faulhaber, J{\"o}rg and Geraud, Cyrill and Guo, Ze and Koch, Philipp and Linke, Miriam and Maurier, Nolwenn and M{\"u}ller, Verena and Thomas, Benjamin and Jochen Sven Utikal and Alamri, Ali Saeed M. and Baczako, Andrea and Berking, Carola and Betke, Matthias and Haas, Carolin and Hartmann, Daniela and Heppt, Markus V. and Kilian, Katharina and Krammer, Sebastian and Lapczynski, Natalie Lidia and Mastnik, Sebastian and Nasifoglu, Suzan and Ruini, Cristel and Sattler, Elke and Schlaak, Max and Wolff, Hans and Achatz, Birgit and Bergbreiter, Astrid and Drexler, Konstantin and Ettinger, Monika and Haferkamp, Sebastian and Halupczok, Anna and Hegemann, Marie and Dinauer, Verena and Maagk, Maria and Mickler, Marion and Philipp, Biance and Wilm, Anna and Wittmann, Constanze and Gesierich, Anja and Glutsch, Valerie and Kahlert, Katrin and Kerstan, Andreas and Schilling, Bastian and Schr{\"u}fer, Philipp} } @article {2019, title = {Does the use of synchrony and artificial intelligence in video interviews affect interview ratings and applicant attitudes?}, journal = {Computers in Human Behavior}, volume = {98}, year = {2019}, month = {Jan-09-2019}, pages = {93 - 101}, issn = {07475632}, doi = {10.1016/j.chb.2019.04.012}, author = {Suen, Hung-Yue and Chen, Mavis Yi-Ching and Lu, Shih-Hao} } @article {2019, title = {The future of health care: Protocol for measuring the potential of task automation grounded in the national health service primary care system}, journal = {JMIR Research Protocols}, volume = {8}, year = {2019}, month = {Jan-01-2019}, pages = {e11232}, doi = {10.2196/11232}, author = {Willis, Matthew and Duckworth, Paul and Coulter, Angela and Meyer, Eric T and Osborne, Michael} } @conference {2019, title = {The future of human-AI collaboration: A taxonomy of design knowledge for hybrid intelligence systems}, booktitle = {Hawaii International Conference on System Sciences (HICSS)}, year = {2019}, month = {09/2018}, abstract = {Recent technological advances, especially in the field of machine learning, provide astonishing progress on the road towards artificial general intelligence. However, tasks in current real-world business applications cannot yet be solved by machines alone. We, therefore, identify the need for developing socio-technological ensembles of humans and machines. Such systems possess the ability to accomplish complex goals by combining human and artificial intelligence to collectively achieve superior results and continuously improve by learning from each other. Thus, the need for structured design knowledge for those systems arises. Following a taxonomy development method, this article provides three main contributions: First, we present a structured overview of interdisciplinary research on the role of humans in the machine learning pipeline. Second, we envision hybrid intelligence systems and conceptualize the relevant dimensions for system design for the first time. Finally, we offer useful guidance for system developers during the implementation of such applications.}, url = {https://www.alexandria.unisg.ch/publications/254994}, author = {Dominik Dellermann and Adrian Calma and Nikolaus Lipusch and Thorsten Weber and Sascha Weigel and Philipp Ebel} } @article {2019, title = {The future of women at work}, volume = {McKinsey Global Institute}, year = {2019}, month = {06/2019}, keywords = {consulting reports}, url = {https://www.mckinsey.com/~/media/mckinsey/featured\%20insights/gender\%20equality/the\%20future\%20of\%20women\%20at\%20work\%20transitions\%20in\%20the\%20age\%20of\%20automation/mgi-the-future-of-women-at-work-full-report-june\%202019.ashx}, author = {Anu Madgavkar and James Manyika and Mekala Krishnan and Kweilin Ellingrud and Lareina Yee and Jonathan Woetzel and Michael Chui and Vivian Hunt and Sruti Balakrishnan} } @article {2019, title = {How 5 data dynamos do their jobs}, year = {2019}, url = {https://www.nytimes.com/2019/06/12/reader-center/data-reporting-spreadsheets.html}, author = {Lindsey Rogers Cook} } @proceedings {9999, title = {Impacts of machine learning on work}, year = {2019}, address = {Wailea, HI}, abstract = {

The increased pervasiveness of technological advancements in automation makes it urgent to address the question of how work is changing in response. Focusing on applications of machine learning (ML) that automate information tasks, we present a simple framework for identifying the impacts of an automated system on a task. From an analysis of popular press articles about ML, we develop 3 patterns for the use of ML--decision support, blended decision making and complete automation--with implications for the kinds of tasks and systems. We further consider how automation of one task might have implications for other interdependent tasks. Our main conclusion is that designers have a range of options for systems and that automation of tasks is not the same as automation of work.

}, keywords = {artificial intelligence, automation, machine learning, work design}, doi = {10.24251/HICSS.2019.719}, url = { http://hdl.handle.net/10125/60031}, attachments = {https://waim.network/sites/crowston.syr.edu/files/Impacts_of_machine_learning_on_work__revision_.pdf}, author = {Kevin Crowston and Bolici, Francesco} } @conference {2019, title = {Inferring work task automatability from AI expert evidence}, booktitle = {the 2019 AAAI/ACM ConferenceProceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society - AIES {\textquoteright}19}, year = {2019}, pages = {485 - 491}, publisher = {ACM Press}, organization = {ACM Press}, address = {Honolulu, HI, USANew York, New York, USA}, isbn = {9781450363242}, doi = {10.1145/330661810.1145/3306618.3314247}, author = {Duckworth, Paul and Graham, Logan and Osborne, Michael} } @article {2019, title = {Introduction: The Future of Jobs in an Increasingly Autonomous Economy}, journal = {Journal of Management Inquiry}, volume = {28}, year = {2019}, month = {Jun-07-2021}, pages = {298 - 299}, issn = {1056-4926}, doi = {10.1177/1056492619827373}, author = {Choi, David Y. and Kang, Jae Hyeung} } @article {2019, title = {Labor, technology and work organization: An introduction to the forum}, journal = {Journal of Industrial and Business Economics}, volume = {46}, year = {2019}, month = {Jan-09-2019}, pages = {313 - 321}, issn = {0391-2078}, doi = {10.1007/s40812-019-00126-w}, author = {Cirillo, Valeria and Molero Zayas, Jos{\'e}} } @inbook {2019, title = {Machine learning for clinical psychology and clinical neuroscience}, booktitle = {researchgate.net}, year = {2019}, abstract = {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.}, url = {https://www.researchgate.net/publication/331000572_Machine_Learning_for_Clinical_Psychology_and_Clinical_Neuroscience}, author = {Marc N. Countanche and Lauren S. Hallion} } @article {2019, title = {Massive technological unemployment without redistribution: A case for cautious optimism}, journal = {Science and Engineering Ethics}, volume = {25}, year = {2019}, month = {Jan-10-2019}, pages = {1389 - 1407}, issn = {1353-3452}, doi = {10.1007/s11948-018-0070-0}, author = {Chomanski, Bartek} } @article {2019, title = {Negotiated and reciprocal exchange structures in human-agent cooperation}, journal = {Computers in Human Behavior}, volume = {90}, year = {2019}, month = {Jan-01-2019}, pages = {288 - 297}, issn = {07475632}, doi = {10.1016/j.chb.2018.08.012}, author = {Chiou, Erin K. and Lee, John D. and Su, Tianshuo} } @article {2019, title = {Net job creation in an increasingly autonomous economy: The challenge of a generation}, journal = {Journal of Management Inquiry}, volume = {28}, year = {2019}, month = {Jun-07-2021}, pages = {300 - 305}, issn = {1056-4926}, doi = {10.1177/1056492619827372}, author = {Choi, David Y. and Kang, Jae Hyeung} } @article {2019, title = {Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis}, journal = {Trends in Cancer}, volume = {5}, year = {2019}, month = {Jan-03-2019}, pages = {157 - 169}, issn = {24058033}, doi = {10.1016/j.trecan.2019.02.002}, author = {Levine, Adrian B. and Schlosser, Colin and Grewal, Jasleen and Coope, Robin and Jones, Steve J.M. and Yip, Stephen} } @article {2019, title = {Scaling up analogical innovation with crowds and AI}, journal = {Proceedings of the National Academy of Sciences}, volume = {116}, year = {2019}, pages = {1870-1877}, issn = {0027-8424}, doi = {10.1073/pnas.1807185116}, author = {Kittur, Aniket and Yu, Lixiu and Hope, Tom and Chan, Joel and Lifshitz-Assaf, Hila and Gilon, Karni and Ng, Felicia and Kraut, Robert E. and Shahaf, Dafna} } @article {2019, title = {The solution lies in education: Artificial intelligence \& the skills gap}, journal = {On the Horizon}, volume = {27}, year = {2019}, month = {Nov-03-2019}, pages = {1 - 4}, issn = {1074-8121}, doi = {10.1108/OTH-03-2019-096}, author = {Chrisinger, David} } @book {2019, title = {Steps toward a scaffolding design framework}, year = {2019}, pages = {149 - 161}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, isbn = {978-3-030-12333-8}, issn = {1571-5035}, doi = {10.1007/978-3-030-12334-5_5}, author = {Correia, Ant{\'o}nio and Jameel, Shoaib and Paredes, Hugo and Fonseca, Benjamim and Schneider, Daniel}, editor = {Khan, Vassillis-Javed and Papangelis, Konstantinos and Lykourentzou, Ioanna and Markopoulos, Panos} } @article {2019, title = {Ten ways the precautionary principle undermines progress in artificial intelligence}, year = {2019}, month = {02/2019}, keywords = {consulting reports}, url = {https://itif.org/publications/2019/02/04/ten-ways-precautionary-principle-undermines-progress-artificial-intelligence}, author = {Daniel Castro and Michael McLaughlin} } @article {Frank2019, title = {Toward understanding the impact of artificial intelligence on labor}, journal = {Proceedings of the National Academy of Sciences}, volume = {116}, year = {2019}, month = {apr}, pages = {6531{\textendash}6539}, abstract = {

Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human{\textendash}machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.

}, issn = {0027-8424}, doi = {10.1073/pnas.1900949116}, url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1900949116}, author = {Frank, Morgan R and Autor, David and Bessen, James E and Brynjolfsson, Erik and Cebrian, Manuel and Deming, David J and Feldman, Maryann and Groh, Matthew and Lobo, Jos{\'e} and Moro, Esteban and Wang, Dashun and Youn, Hyejin and Rahwan, Iyad} } @article {2019, title = {Transforming the communication between citizens and government through AI-guided chatbots}, journal = {Government Information Quarterly}, volume = {36}, year = {2019}, month = {Jan-04-2019}, pages = {358 - 367}, issn = {0740624X}, doi = {10.1016/j.giq.2018.10.001}, url = {https://doi.org/10.1016/j.giq.2018.10.001}, author = {Androutsopoulou, Aggeliki and Karacapilidis, Nikos and Loukis, Euripidis and Charalabidis, Yannis} } @article {2019, title = {Use of Artificial Intelligence to Improve Resilience and Preparedness Against Adverse Flood Events}, journal = {Water}, volume = {11}, year = {2019}, month = {Jan-05-2019}, pages = {973}, doi = {10.3390/w11050973}, author = {Saravi, Sara and Kalawsky, Roy and Joannou, Demetrios and Rivas Casado, Monica and Fu, Guangtao and Meng, Fanlin} } @article {2019, title = {Will machine learning end the viability of radiology as a thriving medical specialty?}, journal = {The British Journal of Radiology}, volume = {92}, year = {2019}, month = {Jan-02-2019}, pages = {20180416}, issn = {0007-1285}, doi = {10.1259/bjr.20180416}, author = {Chan, Stephen and Siegel, Eliot L} } @article {2018, title = {Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review}, journal = {Journal of Affective Disorders}, volume = {241}, year = {2018}, pages = {519-532}, issn = {01650327}, doi = {10.1016/j.jad.2018.08.073}, author = {Lee, Yena and Ragguett, Renee-Marie and Mansur, Rodrigo B. and Boutilier, Justin J. and Rosenblat, Joshua D. and Trevizol, Alisson and Brietzke, Elisa and Lin, Kangguang and Pan, Zihang and Subramaniapillai, Mehala and Chan, Timothy C.Y. and Fus, Dominika and Park, Caroline and Musial, Natalie and Zuckerman, Hannah and Chen, Vincent Chin-Hung and Ho, Roger and Rong, Carola and McIntyre, Roger S.} } @article {2018, title = {Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration}, journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences}, volume = {376}, year = {2018}, month = {Jan-09-2019}, pages = {20170357}, issn = {1364-503X}, doi = {10.1098/rsta.2017.0357}, author = {Mikhaylov, Slava Jankin and Esteve, Marc and Campion, Averill} } @article {2018, title = {As s essing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour}, year = {2018}, url = {https://arxiv.org/ftp/arxiv/papers/1806/1806.03192.pdf}, author = {Emilia G{\'o}mez and Carlos Castillo and Vicky Charisi and Ver{\'o}nica Dahl and Gustavo Deco and Blagoj Delipetrev and Nicole Dewandre and Miguel {\'A}ngel Gonz{\'a}lez-Ballester and Fabien Gouyon and Jos{\'e} Hern{\'a}ndez-Orallo and Perfecto Herrera and Anders Jonsson and Ansgar Koene and Martha Larson and Ram{\'o}n L{\'o}pez de M{\'a}ntaras and Bertin Martens and Marius Miron and Rub{\'e}n Moreno-Bote and Nuria Oliver and Antonio Puertas Gallardo and Heike Schweitzer and Nuria Sebastian and Xavier Serra and Joan Serr{\`a} and Song{\"u}l Tolan and Karina Vold} } @article {2018, title = {Automated Classification of chest X-ray images as normal or abnormal using Convolutional Neural Network}, journal = {Asian Journal of Convergence in Technology}, volume = {4}, year = {2018}, month = {04/2018}, abstract = {Chest X-Rays are generally used for diagnosing abnormalities in the thoracic area. Radiologists need to spend significant amount of time for interpreting scans. Automatic classification of these images could greatly help radiology interpretation process by enhancing real world diagnosis of problems. Hence, radiologists can focus on detecting abnormalities from the abnormal images rather than checking for it in all the images. In this paper, we present a machine learning approach to solve this problem. Here, the algorithm uses COnvolutional Neural Networks (CNN) to learn and classify chest X-ray images as normal or abnormal based on image features.}, keywords = {classification, machine learning, radiology}, author = {Aayushi Gupta and Anupama C and P Indumathi and Anuj Kumar} } @article {2018, title = {Autonomous vehicles employment impact study}, year = {2018}, month = {09/2018}, url = {https://advi.org.au/wp-content/uploads/2018/09/Autonomous-Vehicles-Employment-Impact-Survey-COR050918-5.pdf}, author = {Brian Haratsis and Tony Carmichael and Mark Coutney and Jacob Fong} } @article {2018, title = {Boundary spanning at the science{\textendash}policy interface: the practitioners{\textquoteright} perspectives}, journal = {Sustainability Science}, volume = {13}, year = {2018}, month = {Jan-07-2018}, pages = {1175 - 1183}, issn = {1862-4065}, doi = {10.1007/s11625-018-0550-9}, author = {Bednarek, A. T. and Wyborn, C. and Cvitanovic, C. and Meyer, R. and Colvin, R. M. and Addison, P. F. E. and Close, S. L. and Curran, K. and Farooque, M. and Goldman, E. and Hart, D. and Mannix, H. and McGreavy, B. and Parris, A. and Posner, S. and Robinson, C. and Ryan, M. and Leith, P.} } @book {2018, title = {Creativity, the arts, and the future of work}, year = {2018}, pages = {283 - 310}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-78579-0}, doi = {10.1007/978-3-319-78580-6_9}, author = {Nathan, Linda F.}, editor = {Cook, Justin W.} } @article {2018, title = {Cryptocurrency, Artificial Intelligence and basic income as innovative technological system}, journal = {Path of Science}, volume = {4}, year = {2018}, month = {Jul-08-2020}, pages = {2024 - 2030}, doi = {10.22178/pos10.22178/pos.37-6}, author = {Sopilnyk, Lyubomyr and Shevchuk, Andriy and Kopytko, Vasyl} } @book {2018, title = {Data Science for Undergraduates}, year = {2018}, publisher = {National Academies Press}, organization = {National Academies Press}, address = {Washington, D.C.}, isbn = {978-0-309-47559-4}, doi = {10.17226/25104}, url = {https://www.nap.edu/catalog/25104} } @article {2018, title = {DEFINITION, APPLICATION AND INFLUENCE OF ARTI FICIAL INTELLIGENCE ON DESIGN INDUSTRIES}, journal = {Landscape Architecture Frontiers}, volume = {6}, year = {2018}, month = {Jan-01-2018}, pages = {56}, issn = {2095-5405}, doi = {10.15302/J-LAF-20180207}, author = {Linghao Cai and Ling Fan and Wenbo Lai and LONG, Ying and Peng Wang and Xiangyang Xin} } @article {2018, title = {Embracing the sobering reality of technological influences on jobs, employment and human resource development}, journal = {European Journal of Training and Development}, volume = {42}, year = {2018}, month = {Mar-09-2018}, pages = {400 - 416}, issn = {2046-9012}, doi = {10.1108/EJTD-03-2018-0030}, author = {Chuang, Szufang and Graham, Carroll Marion} } @mastersthesis {2018, title = {Examination of cognitive load in the human-machine teaming context}, year = {2018}, month = {06/2018}, abstract = {The Department of Defense (DoD) is increasingly hoping to employ unmanned systems and artificial intelligence to achieve a strategic advantage over adversaries. While some tasks may be suitable for machine substitution, many parts of the DoD{\textquoteright}s mission continue to require boots on the ground and humans in the loop working in interdependent human-machine teams. The commercial unmanned systems marketplace and active UxS and autonomous systems offer military research and acquisitions professionals promising technical solutions, but may integrate poorly in a human-machine team application. The authors developed a framework for analyzing task-to-technology matches and team design for military human-machine teams. The framework is grounded in the cognitive theories of situational awareness and decision making, team dynamics, and functional allocation literature. Additionally, the research recommends developing a shared DoD-wide understanding of autonomous systems terms and taxonomy, and educating operational leaders, acquisitions staff, and executives about realistic expectations and employment of autonomous systems in human-machine environments.}, url = {https://hdl.handle.net/10945/59638}, author = {Clarke, Alan J. and Knudson, Daniel F. III} } @article {2018, title = {Experimental evidence on complimentarities between human capital and machine learning}, year = {2018}, month = {01/2018}, keywords = {economics of automation}, url = {https://hbswk.hbs.edu/item/different-strokes-for-different-folks-experimental-evidence-on-complementarities-between-human-capital-and-machine-learning}, author = {Prithwiraj Choudhury and Evan Starr and Rajshree Agarwal} } @article {2018, title = {Experimental evidence on productivity complementarities}, number = {18-065}, year = {2018}, publisher = {Harvard Business School Working Paper}, keywords = {Human Capital, Performance Productivity, Technological Innovation, Technology Adoption}, url = {https://www.hbs.edu/faculty/Pages/item.aspx?num=53855}, author = {Prithwiraj Choudhury} } @article {2018, title = {Exploiting ability for human adaptation to facilitate improved human-robot interaction and acceptance}, journal = {The Information Society}, volume = {34}, year = {2018}, month = {Mar-05-2020}, pages = {153 - 165}, issn = {0197-2243}, doi = {10.1080/01972243.2018.1444255}, author = {Caleb-Solly, Praminda and Dogramadzi, Sanja and Huijnen, Claire A.G.J. and Heuvel, Herjan van den} } @article {2018, title = {How real is the impact of artificial intelligence? The business information survey 2018}, journal = {Business Information Review}, volume = {35}, year = {2018}, month = {12/2019}, pages = {99 - 115}, keywords = {Artificial Intelligence (AI), blockchain, chatbot, cybersecurity, data economy, data governance, data lakes, data literacy, data quality, data trusts, data value, ethics, information literacy, intelligent virtual agents, machine learning (ML), Robotics}, issn = {0266-3821}, doi = {10.1177/0266382118790150}, url = {http://journals.sagepub.com/doi/10.1177/0266382118790150}, author = {Carter, Denise} } @article {2018, title = {How the anthropormorphization of virtual assistants influences user{\textquoteright}s trust}, journal = {Academy of Management Proceedings}, volume = {2018}, year = {2018}, month = {Jan-08-2018}, pages = {16328}, issn = {0065-0668}, doi = {10.5465/AMBPP.2018.16328abstract}, author = {Crone, Tim and Shafeie Zargar, Mahmood} } @book {2018, title = {How unequal? Insights on inequality }, series = {CEDA {\textendash} the Committee for Economic Development of Australia}, year = {2018}, month = {04/2018}, keywords = {ethics}, isbn = {0 85801 318 5}, url = {https://www.ceda.com.au/CEDA/media/General/Publication/PDFs/CEDA-How-unequal-Insights-on-inequality-April-2018-FINAL_WEB.pdf} } @article {2018, title = {Identifying factors reinforcing robotization: Interactive forces of employment, working hour and wage}, journal = {Sustainability}, volume = {10}, year = {2018}, month = {Jan-02-2018}, pages = {490}, doi = {10.3390/su10020490}, author = {Cho, Joonmo and Kim, Jinha} } @article {2018, title = { The impact of industrial robots on eu employment and wages: A local labour market approach}, year = {2018}, month = {04/2018}, publisher = {bruegel}, keywords = {economics of automation}, url = {https://bruegel.org/2018/04/the-impact-of-industrial-robots-on-eu-employment-and-wages-a-local-labour-market-approach/}, author = {Francesco Chiacchio and Georgios Petropoulos and David Pichler} } @book {2018, title = {Investigating ergonomics in the context of human-robot collaboration as a sociotechnical system}, volume = {784}, year = {2018}, pages = {127 - 135}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-94345-9}, issn = {2194-5357}, doi = {10.1007/978-3-319-94346-6_12}, author = {R{\"u}cker, Daniel and Hornfeck, R{\"u}diger and Paetzold, Kristin}, editor = {Chen, Jessie} } @book {2018, title = {Lecture Notes in Computer ScienceArtificial General IntelligenceTowards a Sociological Conception of Artificial Intelligence}, volume = {10999}, year = {2018}, pages = {130 - 139}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-97675-4}, issn = {0302-9743}, doi = {10.1007/978-3-319-97676-1}, author = {Jakub Mlynar and Hamed S. Alavi and Himanshu Verma and Lorenzo Cantoni}, editor = {Matthew Ikl{\'e} and Arthur Franz and Rafal Rzepka and Ben Goertzel} } @book {2018, title = {Machine Learning for Ecology and Sustainable Natural Resource ManagementUse of Machine Learning (ML) for Predicting and Analyzing Ecological and {\textquoteleft}Presence Only{\textquoteright} Data: An Overview of Applications and a Good Outlook}, year = {2018}, pages = {27 - 61}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-96976-3}, doi = {10.1007/978-3-319-96978-7_2}, author = {Huettmann, Falk and Craig, Erica H. and Herrick, Keiko A. and Baltensperger, Andrew P. and Grant Humphries and Lieske, David J. and Miller, Katharine and Mullet, Timothy C. and Oppel, Steffen and Resendiz, Cynthia and Rutzen, Imme and Schmid, Moritz S. and Suwal, Madan K. and Young, Brian D.}, editor = {Grant Humphries and Magness, Dawn R. and Huettmann, Falk} } @article {2018, title = {Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR)}, journal = {Systematic Reviews}, year = {2018}, month = {2018}, publisher = {Springer}, type = {Review}, abstract = {Systematic reviews (SR) are vital to health care, but have become complicated and time-consuming, due to the rapid expansion of evidence to be synthesised. Fortunately, many tasks of systematic reviews have the potential to be automated or may be assisted by automation. Recent advances in natural language processing, text mining and machine learning have produced new algorithms that can accurately mimic human endeavour in systematic review activity, faster and more cheaply. Automation tools need to be able to work together, to exchange data and results. Therefore, we initiated the International Collaboration for the Automation of Systematic Reviews (ICASR), to successfully put all the parts of automation of systematic review production together. The first meeting was held in Vienna in October 2015. We established a set of principles to enable tools to be developed and integrated into toolkits. This paper sets out the principles devised at that meeting, which cover the need for improvement in efficiency of SR tasks, automation across the spectrum of SR tasks, continuous improvement, adherence to high quality standards, flexibility of use and combining components, the need for a collaboration and varied skills, the desire for open source, shared code and evaluation, and a requirement for replicability through rigorous and open evaluation. Automation has a great potential to improve the speed of systematic reviews. Considerable work is already being done on many of the steps involved in a review. The {\textquoteright}Vienna Principles{\textquoteright} set out in this paper aim to guide a more coordinated effort which will allow the integration of work by separate teams and build on the experience, code and evaluations done by the many teams working across the globe.}, keywords = {automation, Collaboration, Systematic review}, doi = {10.1186/s13643-018-0740-7}, author = {Elaine Beller and Justin Clark and Guy Tsafnat and Clive Adams and Heinz Diehl and Hans Lund and Mourad Ouzzani and Kristina Thayer and James Thomas and Tari Turner and Jun Xia and Karen Robinson and Paul Glasziou} } @article {2018, title = {Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR)}, journal = {Systematic Reviews}, volume = {7}, year = {2018}, month = {Jan-12-2018}, keywords = {science}, doi = {10.1186/s13643-018-0740-7}, author = {Elaine Beller and Justin Clark and Guy Tsafnat and Clive Adams and Heinz Diehl and Hans Lund and Mourad Ouzzani and Kristina Thayer and James Thomas and Tari Turner and Jun Xia and Karen Robinson and Paul Glasziou} } @article {2018, title = {A principled machine learning framework improves accuracy of stage II colorectal cancer prognosis}, journal = {npj Digital Medicine}, volume = {1}, year = {2018}, month = {Jan-12-2018}, doi = {10.1038/s41746-018-0057-x}, author = {Dimitriou, Neofytos and Arandjelovi{\'c}, Ognjen and Harrison, David J. and Caie, Peter D.} } @article {2018, title = {Reflections on the meaning of automated education}, journal = {education policy analysis archives}, volume = {26}, year = {2018}, month = {May-01-2018}, pages = {115}, doi = {10.14507/epaa.26.3863}, author = {Coelho, Heitor} } @article {2018, title = {Robot Assisted Surgical Ward Rounds: Virtually Always There}, journal = {Journal of Innovation in Health Informatics}, volume = {25}, year = {2018}, month = {Sep-03-2018}, pages = {041}, issn = {2058-4555}, doi = {10.14236/jhi.v25i1.982}, author = {Stefanie M. Croghan and Paul Carroll and Sarah Reade and Amy E Gillis and Paul F. Ridgway} } @article {2018, title = {Robot vs. tax inspector or how the fourth industrial revolution will change the tax system: a review of problems and solutions}, journal = {Journal of Tax Reform}, volume = {4}, year = {2018}, month = {Jan-01-2018}, pages = {6 - 26}, abstract = {The fourth Industiral Revolution and the accelerated development of cyber-physical technologies lead to essential changes in national tax systems and international taxation. The main areas in which taxation meets cyber-physical technologies are digitization, robotization, M2M and blockchain technologies. Each of these areas has its own opportunities and problems. Three main approaches towards possible solutions for these new problems are identified. The first is to try to apply taxation to new cyber-physical technologies and products of their application. This approach includes the OECD{\textquoteright}s Action 1 plan on Base Erosion and Profit Shifting. It also includes the spread of traditional taxes on new objects - personal data, cryptocurrencies, imputed income of robots. The second is to replace digital transactions and shortfalls in revenues by traditional objects of taxation in the form of tangible assets and people and / or increase tax pressure (including by improving tax administration with use of Big Data) and the degree of progressiveness of taxes already levied on such objects. The third approach is to set a course on building a new tax space with smart taxes based on real-time principles, smart contracts and Big Data. This implies a transition to automatic taxation using blockchain technologies, which focus on the functions of applying distributed ledgers of business transactions in real-time. At present, the general trends are such that the first and second are prevalent, which is manifested in an increase in the relative importance of property, sales and employment taxes. Concerning the third approach, any movement in this direction is still facing a number oftechnical and other problems and is thus being discussed mainly at the conceptual level}, keywords = {blockchain, cyber-physical technologies, digitization, taxes in Big Data, taxes on cryptocurrencies, taxes on digital goods, taxes on robots}, issn = {24128872}, doi = {10.15826/jtr.2018.4.1.042}, url = {https://jtr.urfu.ru/en/archive/journal/95/article/1113/}, author = {Vishnevsky, Valentine P. and Chekina, Viktoriia D.} } @article {2018, title = { Robots worldwide: The impact of automation on employment and trade}, number = {36}, year = {2018}, month = {10/2018}, keywords = {economics of automation, Employment, off-shoring, re-shoring, robot, technology}, url = {https://www.ilo.org/wcmsp5/groups/public/---dgreports/---inst/documents/publication/wcms_648063.pdf}, author = {Francesco Carnonero and Ekkehard Ernst and Enzo Weber} } @book {2018, title = {The role of technological progress and structural change in the labour market}, year = {2018}, pages = {15 - 41}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-90547-1}, issn = {2511-2023}, doi = {10.1007/978-3-319-90548-8_2}, author = {Bosio, Giulio and Cristini, Annalisa}, editor = {Bosio, Giulio and Minola, Tommaso and Origo, Federica and Tomelleri, Stefano} } @book {2018, title = {The social consequences of the digital revolution}, volume = {6}, year = {2018}, publisher = {Edizioni Ca{\textquoteright} Foscari}, organization = {Edizioni Ca{\textquoteright} Foscari}, address = {Venice}, isbn = {978-88-6969-274-1}, issn = {2610-9085}, doi = {10.30687/2610-968910.30687/978-88-6969-273-410.30687/978-88-6969-273-4/008}, url = {http://edizionicafoscari.unive.it/collane/societa-e-trasformazioni-sociali/http://edizionicafoscari.unive.it/libri/978-88-6969-274-1/http://edizionicafoscari.unive.it/libri/978-88-6969-274-1/the-social-consequences-of-the-digital-revolution/}, author = {Basso, Pietro and Chiaretti, Giuliana and Krzywdzinski, Martin and Gerber, Christine and Evers, Maren} } @conference {2018, title = {Towards a Sociological Conception of Artificial Intelligence}, booktitle = {Artificial General Intelligence (AGI)}, year = {2018}, publisher = {Springer}, organization = {Springer}, abstract = {Social sciences have been always formed and influenced by the development of society, adjusting the conceptual, methodological, and theoretical frameworks to emerging social phenomena. In recent years, with the leap in the advancement of Artificial Intelligence (AI) and the proliferation of its everyday applications, "non-human intelligent actors" are increasingly becoming part of the society. This is manifested in the evolving realms of smart home systems, autonomous vehicles, chatbots, intelligent public displays, etc. In this paper, we present a prospective research project that takes one of the pioneering steps towards establishing a "distinctively sociological" conception of AI. Its first objective is to extract the existing conceptions of AI as perceived by its technological developers and (possibly differently) by its users. In the second part, capitalizing on a set of interviews with experts from social science domains, we will explore the new imaginable conceptions of AI that do not originate from its technological possibilities but rather from societal necessities. The current formal ways of defining AI are grounded in the technological possibilities, namely, machine learning methods and neural network models. Buy what exactly is AI as a social phenomenon, which may act on its own, can be blamed responsible for ethically problematic behavior, or even endanger people{\textquoteright}s employment? We argue that such conceptual investigation is a crucial step for further empirical studies of phenomena related to AI{\textquoteright}s position in current societies, but also will open up ways for critiques of new technological advancements with social consequences in mind from the outset.}, keywords = {artificial intelligence, Social sciences, Sociology}, doi = {10.1007/978-3-319-97676-1_13}, author = {Jakub Mlynar and Hamed S. Alavi and Himanshu Verma and Lorenzo Cantoni} } @article {2018, title = {An Uber ethical dilemma: examining the social issues at stake}, journal = {Journal of Information, Communication and Ethics in Society}, volume = {16}, year = {2018}, month = {Jan-08-2019}, pages = {261 - 274}, issn = {1477-996X}, doi = {10.1108/JICES-03-2018-0024}, url = {https://www.emerald.com/insight/content/doi/10.1108/JICES-03-2018-0024/full/htmlhttps://www.emeraldinsight.com/doi/full/10.1108/JICES-03-2018-0024https://www.emeraldinsight.com/doi/full-xml/10.1108/JICES-03-2018-0024}, author = {Chee, Florence M.} } @conference {2018, title = {Understanding Chatbot-mediated Task Management}, booktitle = {the 2018 CHI ConferenceProceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI {\textquoteright}18}, year = {2018}, pages = {1 - 6}, publisher = {ACM Press}, organization = {ACM Press}, address = {Montreal QC, CanadaNew York, New York, USA}, isbn = {9781450356206}, doi = {10.1145/317357410.1145/3173574.3173632}, author = {Toxtli, Carlos and Monroy-Hern{\'a}ndez, Andr{\'e}s and Cranshaw, Justin} } @book {2018, title = {Understanding tasks, automation, and the national health service}, volume = {10766}, year = {2018}, pages = {544 - 549}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-78104-4}, issn = {0302-9743}, doi = {10.1007/978-3-319-78105-1_60}, author = {Willis, Matt and Meyer, Eric T.}, editor = {Chowdhury, Gobinda and McLeod, Julie and Gillet, Val and Willett, Peter} } @article {2018, title = {Work and organizational psychology looks at the fourth industrial revolution: How to support workers and organizations? }, journal = {Frontiers in Psychology}, volume = {9}, year = {2018}, month = {Apr-11-2020}, doi = {10.3389/fpsyg.2018.02365}, author = {Ghislieri, Chiara and Molino, Monica and Cortese, Claudio G.} } @article {2017, title = {AI AND THE GHOST IN THE MACHINE}, year = {2017}, url = {http://hackaday.com/2017/02/06/ai-and-the-ghost-in-the-machine/}, author = {Cameron Coward} } @book {2017, title = {Artificial Intelligence and the two singularities}, series = {A Chapman \& Hall Book}, year = {2017}, month = {08/2018}, publisher = {EBSCO}, organization = {EBSCO}, edition = {1st}, url = {https://www.amazon.com/Artificial-Intelligence-Singularities-Chapman-Robotics/dp/0815368534}, author = {Calum Chace} } @article {2017, title = {Artificial Intelligence Index}, year = {2017}, url = {https://aiindex.org/2017-report.pdf}, author = {Yoav Shoham and Raymond Perrault and Erik Brynjolfsson and Jack Clark} } @article {2017, title = {Artificial Intelligence: The next digital frontier}, year = {2017}, url = {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}, author = {Jaccques Bughin and Eric Hazan and Sree Ramaswamy and Michael Chui and Tera Allas and Peter Dahlstrom and Nicolaus Henke and Monica Trench} } @proceedings {9999, title = {Blending machine and human learning processes}, year = {2017}, abstract = {

Citizen science projects rely on contributions from volunteers to achieve their scientific goals and so face a dilemma: providing volunteers with explicit training might increase the quality of contributions, but at the cost of losing the work done by newcomers during the training period, which for many is the only work they will contribute to the project. Based on research in cognitive science on how humans learn to classify images, we have designed an approach to use machine learning to guide the presentation of tasks to newcomers that help them more quickly learn how to do the image classification task while still contributing to the work of the project. A Bayesian model for tracking this learning is presented.

}, doi = {10.24251/HICSS.2017.009}, url = {http://hdl.handle.net/10125/41159}, attachments = {https://waim.network/sites/crowston.syr.edu/files/training\%20v3\%20to\%20share_0.pdf}, author = {Kevin Crowston and Carsten {\O}sterlund and Lee, Tae Kyoung} } @article {2017, title = {A deep learning approach for quantifying tumor extent}, journal = {Scientific Reports}, volume = {7}, year = {2017}, month = {Jan-06-2017}, doi = {10.1038/srep46450}, author = {Cruz-Roa, Angel and Gilmore, Hannah and Basavanhally, Ajay and Feldman, Michael and Ganesan, Shridar and Shih, Natalie N.C. and Tomaszewski, John and Gonz{\'a}lez, Fabio A. and Madabhushi, Anant} } @article {2017, title = {Exploring the tension between transparency and datification effects of open government IS through the lens of Complex Adaptive Systems}, journal = {The Journal of Strategic Information Systems}, volume = {26}, year = {2017}, month = {Jan-09-2017}, pages = {210 - 232}, issn = {09638687}, doi = {10.1016/j.jsis.2017.07.001}, author = {Marjanovic, Olivera and Cecez-Kecmanovic, Dubravka} } @book {2017, title = {The Frontiers of MACHINE LEARNING}, year = {2017}, url = {https://www.nap.edu/read/25021/chapter/5}, author = {Lisa Casola} } @article {2017, title = {A future that works: Automation, employment and productivity}, year = {2017}, url = {https://www.mckinsey.com/mgi/overview}, author = {James Manyika and Michael Chui and Mehdi Miremadi and Jaccques Bughin and Katy George and Paul Willmott and Martin Dewhurst} } @article {657, title = {Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science}, journal = {Classical and Quantum Gravity}, volume = {34}, year = {2017}, pages = {064003}, type = {Journal Article}, doi = {10.1088/1361-6382/aa5cea}, author = {Michael Zevin and Scott Coughlin and Sara Bahaadini and Emre Besler and Neda Rohani and Sarah Allen and Miriam Cabero and Kevin Crowston and Aggelos Katsaggelos and Shane Larson and Tae Kyoung Lee and Chris Lintott and Tyson Littenberg and Andrew Lundgren and Carsten Oesterlund and Joshua Smith and Laura Trouille and Vicky Kalogera} } @article {2017, title = {How fortune 500 firms are adopting online freelancing platforms}, year = {2017}, institution = {University of Oxford}, url = {https://www.oii.ox.ac.uk/publications/platform-sourcing.pdf}, author = {Greetje F. Corporaal and Vili Lehdonvirta} } @book {2017, title = {Information Technology and the U.S. Workforce}, year = {2017}, publisher = {National Academies Press}, organization = {National Academies Press}, address = {Washington, D.C.}, isbn = {978-0-309-45402-5}, doi = {10.17226/24649}, url = {https://www.nap.edu/catalog/24649} } @article {2017, title = {Jobs lost, jobs gained: Workforce transactions in a time of automation}, year = {2017}, keywords = {consulting reports}, url = {https://www.mckinsey.com/~/media/mckinsey/featured\%20insights/future\%20of\%20organizations/what\%20the\%20future\%20of\%20work\%20will\%20mean\%20for\%20jobs\%20skills\%20and\%20wages/mgi-jobs-lost-jobs-gained-report-december-6-2017.ashx}, author = {James Manyika and Susan Lund and Michael Chui and Jonathan Woetzel and Ryan Ko and Saurabh Sanghvi and Parul Batra and Jacques Bughin} } @article {2017, title = {Rapid Evidence Review: Impact of artificial intelligence, robotics and automation technologies on work}, year = {2017}, institution = {Chartered Institute of Personnel and Development}, address = {London, United Kingdom}, abstract = {The CIPD and Loughborough University{\textquoteright}s report gathers the evidence and insights on emerging technology at work and explores the ethical implications of how we{\textquoteright}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?}, url = {https://www.cipd.co.uk/knowledge/work/technology/artificial-intelligence-workplace-impact}, author = {Donald Hislop and Crispin Coombs and Stanimira Taneva and Sarah Barnard} } @book {2017, title = {Refining the concept of scientific inference when working with big data}, year = {2017}, publisher = {National Academies Press}, organization = {National Academies Press}, address = {Washington, D.C.}, isbn = {978-0-309-45444-5}, doi = {10.17226/24654}, editor = {Wender, Ben A.} } @article {2017, title = {A review and synthesis of the individual work performance literature}, journal = {Academy of Management Annals}, volume = {11}, year = {2017}, month = {Jan-06-2017}, pages = {825 - 885}, issn = {1941-6520}, doi = {10.5465/annals.2015.0151}, author = {Carpini, Joseph A. and Parker, Sharon K. and Griffin, Mark A.} } @conference {2017, title = {SPIE ProceedingsBuilding a framework to manage trust in automation}, booktitle = {SPIE Defense + SecurityMicro- and Nanotechnology Sensors, Systems, and Applications IX}, volume = {10194}, year = {2017}, pages = {101941U}, publisher = {SPIE}, organization = {SPIE}, address = {Anaheim, California, United States}, doi = {10.1117/12.2264245}, author = {Metcalfe, J. S. and Amar R. Marathe and Haynes, B. and Paul, V. J. and Gremillion, G. M. and Drnec, K. and Atwater, C. and Estepp, J. R. and Lukos, J. R. and Carter, E. C. and W. D. Nothwang}, editor = {George, Thomas and Dutta, Achyut K. and Islam, M. Saif} } @article {2016, title = {The digital workforce and the workplace of the future}, journal = {Academy of Management Journal}, volume = {59}, year = {2016}, month = {Jan-06-2016}, pages = {731 - 739}, issn = {0001-4273}, doi = {10.5465/amj.2016.4003}, author = {Colbert, Amy and Yee, Nick and George, Gerard} } @conference {2016, title = {Disrupting developer productivity one bot at a time}, booktitle = {the 2016 24th ACM SIGSOFT International SymposiumProceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering - FSE 2016}, year = {2016}, pages = {928 - 931}, publisher = {ACM Press}, organization = {ACM Press}, address = {Seattle, WA, USANew York, New York, USA}, isbn = {9781450342186}, doi = {10.1145/2950290.2983989}, author = {Storey, Margaret-Anne and Zagalsky, Alexey} } @article {2016, title = {How technology is changing work and organizations}, journal = {Annual Review of Organizational Psychology and Organizational Behavior}, volume = {3}, year = {2016}, month = {Sep-03-2017}, pages = {349 - 375}, issn = {2327-0608}, doi = {10.1146/annurev-orgpsych-041015-062352}, author = {Cascio, Wayne F. and Montealegre, Ramiro} } @article {2016, title = {Humans, robots and values}, journal = {Technology in Society}, volume = {45}, year = {2016}, month = {Jan-05-2016}, pages = {19 - 28}, issn = {0160791X}, doi = {10.1016/j.techsoc.2016.01.002}, author = {Cockshott, Paul and Renaud, Karen} } @article {2016, title = {Machine learning and decision support in critical care}, journal = {Proceedings of the IEEE}, volume = {104}, year = {2016}, month = {Jan-02-2016}, pages = {444 - 466}, issn = {0018-9219}, doi = {10.1109/JPROC.2015.2501978}, author = {Johnson, Alistair E. W. and Ghassemi, Mohammad M. and Nemati, Shamim and Niehaus, Katherine E. and Clifton, David and Clifford, Gari D.} } @book {2015, title = {The big move toward big data in employment}, series = {Employment\& Labor Law Solutions Worldwide}, year = {2015}, publisher = {Littler Mendelson, P.C.}, organization = {Littler Mendelson, P.C.}, url = {https://www.littler.com/files/wp_big_data_8-04-15.pdf}, author = {Aaron Crews} } @article {2015, title = {Decision-making authority, team efficiency and human worker satisfaction in mixed human{\textendash}robot teams}, journal = {Autonomous Robots}, volume = {39}, year = {2015}, month = {Jan-10-2015}, pages = {293 - 312}, issn = {0929-5593}, doi = {10.1007/s10514-015-9457-9}, author = {Gombolay, Matthew C. and Gutierrez, Reymundo A. and Clarke, Shanelle G. and Sturla, Giancarlo F. and Shah, Julie A.} } @article {RN30610, title = {Four fundamentals of workplace automation}, journal = {McKinsey Quarterly}, year = {2015}, month = {November}, pages = {1{\textendash}9}, type = {Magazine Article}, author = {Chui, Michael and Manyika, James and Miremadi, Mehdi} } @conference {2015, title = {Intelligible Models for HealthCare}, booktitle = {the 21th ACM SIGKDD International ConferenceProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD {\textquoteright}15}, year = {2015}, pages = {1721 - 1730}, publisher = {ACM Press}, organization = {ACM Press}, address = {Sydney, NSW, AustraliaNew York, New York, USA}, isbn = {9781450336642}, doi = {10.1145/278325810.1145/2783258.2788613}, author = {Caruana, Rich and Lou, Yin and Gehrke, Johannes and Koch, Paul and Sturm, Marc and Elhadad, Noemie} } @book {2015, title = {Mining programming activity to promote help}, year = {2015}, pages = {23 - 42}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-20498-7}, doi = {10.1007/978-3-319-20499-4_2}, author = {Carter, Jason and Dewan, Prasun}, editor = {Boulus-R{\o}dje, Nina and Ellingsen, Gunnar and Bratteteig, Tone and Aanestad, Margunn and Bj{\o}rn, Pernille} } @article {2015, title = { Users{\textquoteright} reactions to actions of automated programs in Wikipedia}, journal = {Computers in Human Behavior}, volume = {50}, year = {2015}, month = {Jan-09-2015}, pages = {66 - 75}, issn = {07475632}, doi = {10.1016/j.chb.2015.03.078}, author = {Cl{\'e}ment, Maxime and Guitton, Matthieu J.} } @article {2014, title = {Four scenarios exploring the future of youth employment}, year = {2014}, month = {12/2014}, url = {https://assets.rockefellerfoundation.org/app/uploads/20141201215005/FutureofYouthEmployment.pdf}, author = {Caroline Budhan and Abigail Carlton and Evan O{\textquoteright}Donnel} } @article {2013, title = {Quantifying the interdisciplinarity of scientific journals and fields}, journal = {Journal of Informetrics}, volume = {7}, year = {2013}, month = {Jan-04-2013}, pages = {469 - 477}, issn = {17511577}, doi = {10.1016/j.joi.2013.01.007}, url = {https://linkinghub.elsevier.com/retrieve/pii/S1751157713000096https://api.elsevier.com/content/article/PII:S1751157713000096?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S1751157713000096?httpAccept=text/plain}, author = {Silva, F.N. and Rodrigues, F.A. and Oliveira, O.N. and da F. Costa, L.} } @conference {2005, title = {A dialogue agent with adaptive and proactive capabilities}, booktitle = {IEEE/WIC/ACM International Conference on Intelligent Agent TechnologyIEEE/WIC/ACM International Conference on Intelligent Agent Technology}, year = {2005}, month = {09/2005}, pages = {293 - 296}, publisher = {IEEE}, organization = {IEEE}, address = {Compiegne Codex, France}, doi = {10.1109/IAT.2005.8}, author = {Baudoin, F. and Bretier, P. and Corruble, V.} } @book {2003, title = {Handbook of psychology work design}, year = {2003}, publisher = {John Wiley \& Sons, Inc.}, organization = {John Wiley \& Sons, Inc.}, address = {Hoboken, NJ, USA}, doi = {10.1002/0471264385.wei1217}, author = {Morgeson, Frederick P. and Campion, Michael A.}, editor = {Weiner, Irving B.} } @inbook {2003, title = {Work design}, booktitle = {Handbook of Psychology: Industrial and Organizational Psychology}, volume = {12}, year = {2003}, pages = {423{\textendash}452}, publisher = {Wiley}, organization = {Wiley}, address = {Hoboken, NY}, author = {Frederick P. 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