@article {9998, title = {Taming artificial intelligence: A theory of control{\textendash}accountability alignment among AI developers and users}, journal = {Academy of Management Review}, year = {In Press}, abstract = {

The growing agency of artificial intelligence (AI) systems, more specifically systems based on machine learning, has raised concern about the security, safety, and ethical risks of AI use. We argue that core to mitigating AI risks is proper alignment of control and accountability for the stakeholders involved in AI development and use. Control enables and accountability motivates stakeholders to achieve desired and avoid undesired outcomes using AI. However, AI systems{\textquoteright} capabilities for autonomous adaptivity reduce control even for the experts who create them. Moreover, increasing interdependencies between AI development and use render it difficult to unambiguously locate control and accountability. In this paper, we address these challenges for mitigating AI risks by postulating decentralized forms of stakeholder governance and integrative negotiations among stakeholders during the AI life cycle as conducive to aligning control and accountability for AI development and use. Further, we specify that extensive information sharing aided by perspective taking and a shared norm of accountability facilitate integrative negotiation strategies. We conclude by discussing the implications of our theory for management scholarship on the impact of AI and identify promising avenues for future research at micro, meso, and macro levels of analysis.

}, keywords = {accountability, artificial intelligence, control, risk, stakeholder negotiation}, doi = {10.5465/amr.2023.0117}, author = {Gudela Grote and Sharon Parker and Kevin Crowston} } @article {Noonpakdee2020, title = {The Adoption of Artificial Intelligence for Financial Investment Service}, journal = {2020 22nd International Conference on Advanced Communication Technology (ICACT)}, year = {2020}, pages = {396{\textendash}400}, publisher = {Global IT Research Institute - GIRI}, keywords = {AI, artificial intelligence, data, financial, fintech, investment service, Technology Adoption, to investment companies, to offer customers accessibility}, isbn = {9791188428045}, author = {Noonpakdee, Wasinee} } @article {Sidaoui2020, title = {AI feel you: customer experience assessment via chatbot interviews}, journal = {Journal of Service Management}, year = {2020}, abstract = {Purpose: While customer experience (CE) is recognized as a critical determinant of business success, both academics and managers are yet to find a means to gain a comprehensive understanding of CE cost-effectively. The authors argue that the application of relevant AI technology could help address this challenge. Employing interactively prompted narrative storytelling, and the authors investigate the effectiveness of sentiment analysis (SA) on extracting valuable CE insights from primary qualitative data generated via chatbot interviews. Design/methodology/approach: Drawing on a granular and semantically clear framework for studying CE feelings, an artificial intelligence (AI) augmented chatbot was designed. The chatbot interviewed a crowdsourced sample of consumers about their recalled service experience feelings. By combining free-text and closed-ended questions, the authors were able to compare extracted sentiment polarities against established measurement scales and empirically validate our novel approach. Findings: The authors demonstrate that SA can effectively extract CE feelings from primary chatbot data. This findings also suggest that further enhancement in accuracy can be achieved via improvements in the interplay between the chatbot interviewer and SA extraction algorithms. Research limitations/implications: The proposed customer-centric approach can help service companies to study and better understand CE feelings in a cost-effective and scalable manner. The AI-augmented chatbots can also help companies to foster immersive and engaging relationships with customers. This study focuses on feelings, warranting further research on AI{\textquoteright}s value in studying other CE elements. Originality/value: The unique inquisitive role of AI-infused chatbots in conducting interviews and analyzing data in realtime, offers considerable potential for studying CE and other subjective constructs.}, keywords = {artificial intelligence, chatbot, Customer experience, Customer feelings, Sentiment analysis, Storytelling}, issn = {17575818}, doi = {10.1108/JOSM-11-2019-0341}, author = {Sidaoui, Karim and Jaakkola, Matti and Burton, Jamie} } @article {Kuziemski2020, title = {AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings}, journal = {Telecommunications Policy}, volume = {44}, number = {6}, year = {2020}, pages = {101976}, publisher = {Elsevier Ltd}, abstract = {The rush to understand new socio-economic contexts created by the wide adoption of AI is justified by its far-ranging consequences, spanning almost every walk of life. Yet, the public sector{\textquoteright}s predicament is a tragic double bind: its obligations to protect citizens from potential algorithmic harms are at odds with the temptation to increase its own efficiency - or in other words - to govern algorithms, while governing by algorithms. Whether such dual role is even possible, has been a matter of debate, the challenge stemming from algorithms{\textquoteright} intrinsic properties, that make them distinct from other digital solutions, long embraced by the governments, create externalities that rule-based programming lacks. As the pressures to deploy automated decision making systems in the public sector become prevalent, this paper aims to examine how the use of AI in the public sector in relation to existing data governance regimes and national regulatory practices can be intensifying existing power asymmetries. To this end, investigating the legal and policy instruments associated with the use of AI for strenghtening the immigration process control system in Canada; {\textquotedblleft}optimising{\textquotedblright} the employment services{\textquotedblright} in Poland, and personalising the digital service experience in Finland, the paper advocates for the need of a common framework to evaluate the potential impact of the use of AI in the public sector. In this regard, it discusses the specific effects of automated decision support systems on public services and the growing expectations for governments to play a more prevalent role in the digital society and to ensure that the potential of technology is harnessed, while negative effects are controlled and possibly avoided. This is of particular importance in light of the current COVID-19 emergency crisis where AI and the underpinning regulatory framework of data ecosystems, have become crucial policy issues as more and more innovations are based on large scale data collections from digital devices, and the real-time accessibility of information and services, contact and relationships between institutions and citizens could strengthen {\textendash} or undermine - trust in governance systems and democracy.}, keywords = {Algorithmic accountability, artificial intelligence, Automated decision making, Public sector innovation}, issn = {03085961}, doi = {10.1016/j.telpol.2020.101976}, url = {https://doi.org/10.1016/j.telpol.2020.101976}, author = {Kuziemski, Maciej and Misuraca, Gianluca} } @article {Dogru2020, title = {AI in operations management: applications, challenges and opportunities}, journal = {Journal of Data, Information and Management}, volume = {2}, number = {2}, year = {2020}, pages = {67{\textendash}74}, publisher = {Journal of Data, Information and Management}, abstract = {We have witnessed unparalleled progress in artificial intelligence (AI) and machine learning (ML) applications in the last two decades. The AI technologies have accelerated advancements in robotics and automation, which have significant implications on almost every aspect of businesses, and especially supply chain operations. Supply chains have widely adopted smart technologies that enable real-time automated data collection, analysis, and prediction. In this study, we review recent applications of AI in operations management (OM) and supply chain management (SCM). Specifically, we consider the innovations in healthcare, manufacturing, and retail operations, since collectively, these three areas represent a majority of the AI innovations in business as well as growing problem areas. We discuss primary challenges and opportunities for utilizing AI in those industries. We also discuss trending research topics with significant value potential in these areas.}, keywords = {AI, artificial intelligence, Artificial Intelligence (AI), automation, machine learning, machine learning (ML), ml, om, operations management, Operations Management (OM), Robotics, scm, supply chain management, Supply Chain Management (SCM)}, issn = {2524-6356}, doi = {10.1007/s42488-020-00023-1}, author = {Dogru, Ali K and Keskin, Burcu B} } @article {Black2020, title = {AI-enabled recruiting: What is it and how should a manager use it?}, journal = {Business Horizons}, volume = {63}, number = {2}, year = {2020}, pages = {215{\textendash}226}, publisher = {Elsevier Ltd}, abstract = {AI-enabled recruiting systems have evolved from nice to talk about to necessary to utilize. In this article, we outline the reasons underlying this development. First, as competitive advantages have shifted from tangible to intangible assets, human capital has transitioned from supporting cast to a starring role. Second, as digitalization has redesigned both the business and social landscapes, digital recruiting of human capital has moved from the periphery to center stage. Third, recent and near-future advances in AI-enabled recruiting have improved recruiting efficiency to the point that managers ignore them or procrastinate their utilization at their own peril. In addition to explaining the forces that have pushed AI-enabled recruiting systems from nice to necessary, we outline the key strategic steps managers need to take in order to capture its main benefits.}, keywords = {AI-enabled recruiting, artificial intelligence, Digital recruiting technology, Human resources}, issn = {00076813}, doi = {10.1016/j.bushor.2019.12.001}, url = {https://doi.org/10.1016/j.bushor.2019.12.001}, author = {Black, J Stewart and van Esch, Patrick} } @conference {Sushina2020, title = {Artificial Intelligence in the Criminal Justice System: Leading Trends and Possibilities}, booktitle = {Proceedings of the 6th International Conference on Social, economic, and academic leadership (ICSEAL-6-2019)}, volume = {441}, year = {2020}, pages = {432{\textendash}437}, publisher = {Atlantis Press}, organization = {Atlantis Press}, address = {Paris, France}, keywords = {artificial intelligence, criminal justice system, digital technologies, Leadership}, isbn = {978-94-6252-974-8}, doi = {10.2991/assehr.k.200526.062}, url = {https://www.atlantis-press.com/article/125940991}, author = {Sushina, Tatyana and Sobenin, Andrew} } @article {Johnson2020, title = {The benefits of eHRM and AI for talent acquisition}, journal = {Journal of Tourism Futures}, year = {2020}, abstract = {Purpose : The hospitality and tourism industry faces a number of workforce challenges, especially the high turnover rates and associated replacement costs associated with continually identifying and hiring new employees. The purpose of this paper is to discuss how hospitality and tourism organizations can use electronic human resource management (eHRM) and artificial intelligence (AI) to help recruit and select qualified employees, increase individual retention rates and decrease the time needed to replace employees. Specifically, it discusses how e-recruiting and e-selection and AI tools can help hospitality and tourism organizations improve recruiting and selection outcomes. Design/methodology/approach: Research on eHRM, AI, employee recruitment and employee selection are applied to the hospitality and tourism industry and insights for how eHRM and AI can be applied to the industry are discussed. Findings: eHRM and AI have the potential to transform how the hospitality and tourism industry recruit and select employees. However, care must be taken to ensure that the insights gained and the decisions made are well received by employees and lead to better employee and organizational outcomes. Research limitations/implications: This paper represents the first research that integrates research from eHRM and AI and applies it to the hospitality and tourism industry. Originality/value: This paper represents the first research that integrates research from eHRM and AI and applies it to the hospitality and tourism industry.}, keywords = {artificial intelligence, e-HRM, e-recruiting, e-selection, eHRM, Electronic human resource management, Employee selection, Recruitment, Selection}, issn = {2055592X}, doi = {10.1108/JTF-02-2020-0013}, author = {Johnson, Richard D and Stone, Dianna L and Lukaszewski, Kimberly M} } @article {Kassens-Noor2020, title = {Cities of the Future? The Potential Impact of Artificial Intelligence}, journal = {AI}, volume = {1}, number = {2}, year = {2020}, pages = {192{\textendash}197}, abstract = {Artificial intelligence (AI), like many revolutionary technologies in human history, will have a profound impact on societies. From this viewpoint, we analyze the combined effects of AI to raise important questions about the future form and function of cities. Combining knowledge from computer science, urban planning, and economics while reflecting on academic and business perspectives, we propose that the future of cities is far from being a determined one and cities may evolve into ghost towns if the deployment of AI is not carefully controlled. This viewpoint presents a fundamentally different argument, because it expresses a real concern over the future of cities in contrast to the many publications who exclusively assume city populations will increase predicated on the neoliberal urban growth paradigm that has for centuries attracted humans to cities in search of work.}, keywords = {artificial intelligence, autonomous vehicle, future, smart cities, work}, issn = {2673-2688}, doi = {10.3390/ai1020012}, author = {Kassens-Noor, Eva and Hintze, Arend} } @article {Acemoglu2020, title = {Competing with Robots: Firm-Level Evidence from France}, journal = {AEA Papers and Proceedings}, volume = {110}, year = {2020}, pages = {383{\textendash}388}, abstract = {We study the firm-level implications of robot adoption in France. Of 55,390 firms in our sample, 598 adopted robots between 2010 and 2015, but these firms accounted for 20 percent of manufacturing employment. Adopters experienced significant declines in labor shares, the share of production workers in employment, and increases in value added and productivity. They expand their overall employment as well. However, this expansion comes at the expense of competitors, leading to an overall negative association between adoption and employment. Robot adoption has a large impact on the labor share because adopters are larger and grow faster than their competitors.}, keywords = {automation, competition, j23, j24, jel codes, l11, labor share, manufacturing, productivity, reallocation, robots, tasks}, issn = {2574-0768}, doi = {10.1257/pandp.20201003}, author = {Acemoglu, Daron and Lelarge, Claire and Restrepo, Pascual} } @article {Zavrsnik2020, title = {Criminal justice, artificial intelligence systems, and human rights}, journal = {ERA Forum}, volume = {20}, number = {4}, year = {2020}, pages = {567{\textendash}583}, publisher = {The Author(s)}, abstract = {The automation brought about by big data analytics, machine learning and artificial intelligence systems challenges us to reconsider fundamental questions of criminal justice. The article outlines the automation which has taken place in the criminal justice domain and answers the question of what is being automated and who is being replaced thereby. It then analyses encounters between artificial intelligence systems and the law, by considering case law and by analysing some of the human rights affected. The article concludes by offering some thoughts on proposed solutions for remedying the risks posed by artificial intelligence systems in the criminal justice domain.}, keywords = {Algorithms, artificial intelligence, automation, Criminal justice, Fair trial, Human rights}, isbn = {1202702000602}, issn = {18639038}, doi = {10.1007/s12027-020-00602-0}, url = {http://dx.doi.org/10.1007/s12027-020-00602-0}, author = {Zavr{\v s}nik, Ale{\v s}} } @conference {Martnez-Plumed2020, title = {Does AI qualify for the job? A bidirectional model mapping labour and AI intensities}, booktitle = {AIES 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society}, year = {2020}, pages = {94{\textendash}100}, abstract = {In this paper we present a setting for examining the relation between the distribution of research intensity in AI research and the relevance for a range of work tasks (and occupations) in current and simulated scenarios. We perform a mapping between labour and AI using a set of cognitive abilities as an intermediate layer. This setting favours a two-way interpretation to analyse (1) what impact current or simulated AI research activity has or would have on labour-related tasks and occupations, and (2) what areas of AI research activity would be responsible for a desired or undesired effect on specific labour tasks and occupations. Concretely, in our analysis we map 59 generic labour-related tasks from several worker surveys and databases to 14 cognitive abilities from the cognitive science literature, and these to a comprehensive list of 328 AI benchmarks used to evaluate progress in AI techniques. We provide this model and its implementation as a tool for simulations. We also show the effectiveness of our setting with some illustrative examples.}, keywords = {AI benchmarks, AI impact, AI intensity, Labour market, Simulation, tasks}, isbn = {9781450371100}, doi = {10.1145/3375627.3375831}, author = {Mart{\textquoteright}nez-Plumed, Fernando and Tolan, Song{\textquoteright}l and Pesole, Annarosa and Hern{\textquoteright}ndez-Orallo, Jos{\'e} and Fern{\textquoteright}ndez-Mac{\textquoteright}as, Enrique and G{\textquoteright}mez, Emilia} } @article {Huang2020, title = {Engaged to a Robot? The Role of AI in Service}, journal = {Journal of Service Research}, year = {2020}, abstract = {This article develops a strategic framework for using artificial intelligence (AI) to engage customers for different service benefits. This framework lays out guidelines of how to use different AIs to engage customers based on considerations of nature of service task, service offering, service strategy, and service process. AI develops from mechanical, to thinking, and to feeling. As AI advances to a higher intelligence level, more human service employees and human intelligence (HI) at the intelligence levels lower than that level should be used less. Thus, at the current level of AI development, mechanical service should be performed mostly by mechanical AI, thinking service by both thinking AI and HI, and feeling service mostly by HI. Mechanical AI should be used for standardization when service is routine and transactional, for cost leadership, and mostly at the service delivery stage. Thinking AI should be used for personalization when service is data-rich and utilitarian, for quality leadership, and mostly at the service creation stage. Feeling AI should be used for relationalization when service is relational and high touch, for relationship leadership, and mostly at the service interaction stage. We illustrate various AI applications for the three major AI benefits, providing managerial guidelines for service providers to leverage the advantages of AI as well as future research implications for service researchers to investigate AI in service from modeling, consumer, and policy perspectives.}, keywords = {artificial intelligence, augmentation, automation, engagement, feeling AI, human intelligence, mechanical AI, personalization, relationalization, replacement, robots, service process, service strategy, standardization, thinking AI}, issn = {15527379}, doi = {10.1177/1094670520902266}, author = {Huang, Ming Hui and Rust, Roland T} } @article {GomezdeAgreda2020, title = {Ethics of autonomous weapons systems and its applicability to any AI systems}, journal = {Telecommunications Policy}, volume = {5}, number = {5}, year = {2020}, pages = {101953}, publisher = {Elsevier Ltd}, abstract = {Most artificial intelligence technologies are dual-use. They are incorporated into both peaceful civilian applications and military weapons systems. Most of the existing codes of conduct and ethical principles on artificial intelligence address the former while largely ignoring the latter. But when these technologies are used to power systems specifically designed to cause harm, the question must be asked as to whether the ethics applied to military autonomous systems should also be taken into account for all artificial intelligence technologies susceptible of being used for those purposes. However, while a freeze in investigations is neither possible nor desirable, neither is the maintenance of the current status quo. Comparison between general-purpose ethical codes and military ones concludes that most ethical principles apply to human use of artificial intelligence systems as long as two characteristics are met: that the way algorithms work is understood and that humans retain enough control. In this way, human agency is fully preserved and moral responsibility is retained independently of the potential dual-use of artificial intelligence technology.}, keywords = {AI ethics, Autonomous weapons, CCW, Dual-use AI, Explainability, Meaningful human control}, issn = {03085961}, doi = {10.1016/j.telpol.2020.101953}, url = {https://doi.org/10.1016/j.telpol.2020.101953}, author = {G{\'o}mez de {\'A}greda, {\'A}ngel} } @article {EganadelSol2020, title = {The Future of Work in Developing Economies}, journal = {MIT Sloan Management Review}, volume = {61}, number = {2}, year = {2020}, pages = {1{\textendash}3}, abstract = {

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

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

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

}, keywords = {artificial intelligence, automation, Coordination, machine learning, work design}, isbn = {978-1-4503-8054-6/20/11}, doi = {10.1145/3406499.3415070}, attachments = {https://waim.network/sites/crowston.syr.edu/files/Impacts_of_ML_for_HAI_2020.pdf}, author = {Kevin Crowston and Bolici, Francesco} } @article {Reim2020, title = {Implementation of Artificial Intelligence (AI): A Roadmap for Business Model Innovation}, journal = {AI}, volume = {1}, number = {2}, year = {2020}, pages = {180{\textendash}191}, abstract = {Technical advancements within the subject of artificial intelligence (AI) leads towards development of human-like machines, able to operate autonomously and mimic our cognitive behavior. The progress and interest among managers, academics and the public has created a hype among many industries, and many firms are investing heavily to capitalize on the technology through business model innovation. However, managers are left with little support from academia when aiming to implement AI in their firm{\textquoteright}s operations, which leads to an increased risk of project failure and unwanted results. This paper aims to provide a deeper understanding of AI and how it can be used as a catalyst for business model innovation. Due to the increasing range and variety of the available published material, a literature review has been performed to gather current knowledge within AI business model innovation. The results are presented in a roadmap to guide the implementation of AI to firm{\textquoteright}s operations. Our presented findings suggest four steps when implementing AI: (1) understand AI and organizational capabilities needed for digital transformation; (2) understand current BM, potential for BMI, and business ecosystem role; (3) develop and refine capabilities needed to implement AI; and (4) reach organizational acceptance and develop internal competencies.}, keywords = {artificial intelligence, business model innovation, business models, implementation}, issn = {2673-2688}, doi = {10.3390/ai1020011}, author = {Reim, Wiebke and {\r A}str{\"o}m, Josef and Eriksson, Oliver} } @conference {Azarova2020, title = {Key Advantages and Risks of Implementing Artificial Intelligence in the Activities of Professional Communicators}, booktitle = {Proceedings of the 2020 IEEE Communication Strategies in Digital Society Seminar, ComSDS 2020}, year = {2020}, pages = {82{\textendash}86}, abstract = {Nowadays we observe a wide penetration of digital technologies into communication experts practice. Literature review demonstrates a great interest of both technical and humanitarian scholars to this subject. The research results, obtained by opinion survey (young adults of 19-24 years old), illustrate possible pragmatic and ethical effects of AI applications as well as their advantages and risks.}, keywords = {AI technologies risks, artificial intelligence, digital technologies, professional communicators}, isbn = {9781728164410}, doi = {10.1109/ComSDS49898.2020.9101238}, author = {Azarova, Liudmila and Kudryavtseva, Maria and Sharakhina, Larisa} } @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} } @article {Kernbach2020, title = {Machine learning-based clinical prediction modeling {\textendash} A practical guide for clinicians}, journal = {Artificial Intelligence in Precision Health}, number = {June}, year = {2020}, pages = {257{\textendash}278}, publisher = {Elsevier Inc.}, keywords = {Alzheimers disease detection, artificial intelligence, Convolutional neural networks, Deep neural networks, Ensemble machine learning methods}, isbn = {9780128171332}, doi = {10.1016/b978-0-12-817133-2.00011-2}, url = {https://arxiv.org/abs/2006.15069v1 http://dx.doi.org/10.1016/B978-0-12-817133-2.00011-2}, author = {Kernbach, Julius M. and Staartjes, Victor E.} } @article {Seeber2020, title = {Machines as teammates: A research agenda on AI in team collaboration}, journal = {Information and Management}, volume = {57}, number = {2}, year = {2020}, pages = {103174}, publisher = {Elsevier}, abstract = {What if artificial intelligence (AI) machines became teammates rather than tools? This paper reports on an international initiative by 65 collaboration scientists to develop a research agenda for exploring the potential risks and benefits of machines as teammates (MaT). They generated 819 research questions. A subteam of 12 converged them to a research agenda comprising three design areas {\textendash} Machine artifact, Collaboration, and Institution {\textendash} and 17 dualities {\textendash} significant effects with the potential for benefit or harm. The MaT research agenda offers a structure and archetypal research questions to organize early thought and research in this new area of study.}, keywords = {artificial intelligence, Design, Duality, Research agenda, Team collaboration}, issn = {03787206}, doi = {10.1016/j.im.2019.103174}, url = {https://doi.org/10.1016/j.im.2019.103174}, author = {Seeber, Isabella and Bittner, Eva and Briggs, Robert O and de Vreede, Triparna and de Vreede, Gert Jan and Elkins, Aaron and Maier, Ronald and Merz, Alexander B and Oeste-Rei{\ss}, Sarah and Randrup, Nils and Schwabe, Gerhard and S{\"o}llner, Matthias} } @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 {Wagner2020, title = {The nature of the Artificially Intelligent Firm - An economic investigation into changes that AI brings to the firm}, journal = {Telecommunications Policy}, volume = {44}, number = {6}, year = {2020}, pages = {101954}, publisher = {Elsevier Ltd}, abstract = {With the arrival of Artificial Intelligence (AI), the nature of the firm is changing and economic theory can provide guidance to businesses as well as to politics when formulating adequate strategies for this unknown terrain. By interpreting AI as a new type of agent within the firm, the theory of the firm can serve as a lingua franca to connect computer sciences and social sciences when dealing with the interdisciplinary phenomenon of AI. To achieve this, this paper adopts the perspective of the economic theory of the firm to systematically explore the changes that AI brings to the institution of the firm. In total, five interrelated propositions are discussed that are rooted in the traditional theory but trace the nature of the Artificially Intelligent Firm: AI intensifies the effects of economic rationality on the firm (1). AI introduces a new type of information asymmetry (2). AI can perforate the boundaries of the firm (3). AI can create triangular agency relationships (4) and AI has the potential to remove traditional limits of integration (5).}, keywords = {artificial intelligence, Asymmetric information, machine learning, Principal-agent problem, Theory of the firm}, issn = {03085961}, doi = {10.1016/j.telpol.2020.101954}, url = {https://doi.org/10.1016/j.telpol.2020.101954}, author = {Wagner, Dirk Nicolas} } @article {Spencer2020, title = {No automation please, we{\textquoteright}re British: Technology and the prospects for work}, journal = {Cambridge Journal of Regions, Economy and Society}, volume = {13}, number = {1}, year = {2020}, pages = {117{\textendash}134}, abstract = {This article assesses the impact and probably limits of automation. It looks, in particular, at the case of the UK economy. The prospects for automation are seen as necessarily uncertain and potentially regressive in their effects, with technology likely to sustain a large number of low-quality jobs. The deep-seated problems of the UK economy-low-investment, low-productivity and low-real wages-are seen as key impediments to forms of automation that work for all in society. It is argued that, without wider institutional reform, the UK will be unable to reap the full potential of automation.}, keywords = {automation, investment, robots, technology, work}, issn = {17521386}, doi = {10.1093/cjres/rsaa003}, author = {Spencer, David and Slater, Gary} } @booklet {Willcocks2020, title = {Robo-Apocalypse cancelled? Reframing the automation and future of work debate}, howpublished = {Journal of Information Technology}, number = {2016}, year = {2020}, abstract = {Robotics and the automation of knowledge work, often referred to as AI (artificial intelligence), are presented in the media as likely to have massive impacts, for better or worse, on jobs skills, organizations and society. The article deconstructs the dominant hype-and-fear narrative. Claims on net job loss emerge as exaggerated, but there will be considerable skills disruption and change in the major global economies over the next 12 years. The term AI has been hijacked, in order to suggest much more going on technologically than can be the case. The article reviews critically the research evidence so far, including the author{\textquoteright}s own, pointing to eight major qualifiers to the dominant discourse of major net job loss from a seamless, overwhelming AI wave sweeping fast through the major economies. The article questions many assumptions: that automation creates few jobs short or long term; that whole jobs can be automated; that the technology is perfectible; that organizations can seamlessly and quickly deploy AI; that humans are machines that can be replicated; and that it is politically, socially and economically feasible to apply these technologies. A major omission in all studies is factoring in dramatic increases in the amount of work to be done. Adding in ageing populations, productivity gaps and skills shortages predicted across many G20 countries, the danger might be too little, rather than too much labour. The article concludes that, if there is going to be a Robo-Apocalypse, this will be from a collective failure to adjust to skills change over the next 12 years. But the debate needs to be widened to the impact of eight other technologies that AI insufficiently represents in the popular imagination and that, in combination, could cause a techno-apocalypse.}, keywords = {AI, automation, cognitive automation, future of work, Information Technology, Jobs, robotic process automation, skills}, isbn = {0268396220925}, issn = {14664437}, doi = {10.1177/0268396220925830}, author = {Willcocks, Leslie} } @article {Petersen2020, title = {The Role of Discretion in the Age of Automation}, journal = {Computer Supported Cooperative Work: CSCW: An International Journal}, volume = {29}, number = {3}, year = {2020}, pages = {303{\textendash}333}, abstract = {This paper examines the nature of discretion in social work in order to debunk myths dominating prevalent debates on digitisation and automation in the public sector. Social workers have traditionally used their discretion widely and with great autonomy, but discretion has increasingly come under pressure for its apparent subjectivity and randomness. In Denmark, our case in point, the government recently planned to standardise laws to limit or remove discretion where possible in order for automation of case management to gain a foothold. Recent studies have focused on discretion in the public sector, but few have examined it explicitly and as part of real cases. As a consequence, they often leave the myths about discretion unchallenged. Inspired by the literature on discretion and CSCW research on rules in action, this study reports on an empirical investigation of discretion in child protection services in Denmark. The results of our analysis provide a new understanding of discretion as a cooperative endeavour, based on consultation and skill, rather than an arbitrary or idiosyncratic choice. In this manner, our study contradicts the myth of discretion inherent in the automation agenda. Correspondingly, we ask for attention to be given to systems that integrate discretion with technology rather than seek to undermine it directly or get around it surreptitiously. In this age of automation, this is not only an important but also an urgent task for CSCW researchers to fulfil.}, keywords = {Administrative work, automation, Casework, Decision-Making, Digital-ready legislation, Digitisation, Discretion, Rules in action, Social work}, issn = {15737551}, doi = {10.1007/s10606-020-09371-3}, author = {Petersen, Anette C.M. and Christensen, Lars Rune and Hildebrandt, Thomas T.} } @article {Theresa2020, title = {Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation}, journal = {The TQM Journal}, volume = {ahead-of-p}, number = {ahead-of-print}, year = {2020}, month = {jan}, abstract = {Purpose This paper explores if and how Artificial Intelligence can contribute to marketing strategy formulation.Design/methodology/approach Qualitative research based on exploratory in-depth interviews with industry experts currently working with artificial intelligence tools.Findings Key themes include: (1) Importance of AI in strategic marketing decision management; (2) Presence of AI in strategic decision management; (3) Role of AI in strategic decision management; (4) Importance of business culture for the use of AI; (5) Impact of AI on the business{\textquoteright} organizational model. A key consideration is a {\textquotedblleft}creative-possibility perspective,{\textquotedblright} highlighting the future potential to use AI not only for rational but also for creative thinking purposes.Research limitations/implications This work is focused only on strategy creation as a deliberate process. For this, AI can be used as an effective response to the external contingencies of high volumes of data and uncertain environmental conditions, as well as being an effective response to the external contingencies of limited managerial cognition. A key future consideration is a {\textquotedblleft}creative-possibility perspective.{\textquotedblright}Practical implications A practical extension of the Gartner Analytics Ascendancy Model (Maoz, 2013).Originality/value This paper aims to contribute knowledge relating to the role of AI in marketing strategy formulation and explores the potential avenues for future use of AI in the strategic marketing process. This is explored through the lens of contingency theory, and additionally, findings are expressed using the Gartner analytics ascendancy model.}, keywords = {AI, artificial intelligence, creativity, marketing strategy, marketing synergy, paper type research paper, rationality, tqm}, isbn = {1754-2731}, doi = {10.1108/TQM-12-2019-0303}, url = {https://doi.org/10.1108/TQM-12-2019-0303}, author = {Theresa, Eriksson and Alessandro, Bigi and Michelle, Bonera and Eriksson, Theresa and Bigi, Alessandro and Bonera, Michelle and Theresa, Eriksson and Alessandro, Bigi and Michelle, Bonera} } @conference {Ferreira2020, title = {Understanding the Impact of Artificial Intelligence on Services}, booktitle = {International Conference on Exploring Services Science}, series = {Lecture Notes in Business Information Processing}, volume = {1}, number = {January}, year = {2020}, pages = {202{\textendash}213}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {The service sector is changing drastically due the use of robotics and other technologies, such as Artificial Intelligence (AI), Internet of things (IoT), Big Data and Biometrics. Consequently, further research opportunities in the service industry domain are also expected. In light of the above, the purpose of this paper is to explore the potentialities and limitations of service robots in the hospitality industry. To this end, this paper uses a conceptual approach based on a literature review. As a result, we found that in contexts of high customer contact, service robots should be considered to perform standardized tasks due to social/emotional and cognitive/analytical complexity. The hospitality industry is therefore considered closely related to empathic intelligence, as the integration of service robots has not yet reached the desired stage of service delivery. In a seemingly far-fetched context of our reality, organizations will have to decide whether the AI will allow the complete replacement of humans with robots capable of performing the necessary cognitive and emotional tasks. Or investing in balanced capacities by integrating robot-human systems that seems a rea- sonable option these days. Keywords:}, keywords = {{\'a} servitization {\'a} design, science research, service design}, isbn = {9783030387242}, doi = {10.1007/978-3-030-38724-2}, url = {http://link.springer.com/10.1007/978-3-030-38724-2}, author = {Ferreira, Pedro and Teixeira, Jorge Grenha and Teixeira, Lu{\'\i}s F.}, editor = {N{\'o}voa, Henriqueta and Dr{\u a}goicea, Monica and K{\"u}hl, Niklas} } @article {Krupiy2020, title = {A vulnerability analysis : Theorising the impact of artificial intelligence decision-making processes on individuals , society and human diversity from a social justice perspective}, journal = {Computer Law \& Security Review: The International Journal of Technology Law and Practice}, volume = {38}, year = {2020}, pages = {105429}, publisher = {Elsevier Ltd}, keywords = {artificial intelligence, data science, Decision-maki}, issn = {0267-3649}, doi = {10.1016/j.clsr.2020.105429}, url = {https://doi.org/10.1016/j.clsr.2020.105429}, author = {Krupiy, Tetyana Tanya} } @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} } @article {2019, title = {Applying for your next job may be an automated nightmare}, journal = {Gizmodo}, year = {2019}, month = {04/2019}, type = {Automation}, keywords = {automation, job}, url = {https://gizmodo.com/applying-for-your-next-job-may-be-an-automated-nightmar-1834275825}, author = {Brian Merchant} } @article {2019, title = {Digitalization and the future of work: Macroeconomic consequences}, year = {2019}, publisher = {ZEW - Leibniz-Zentrum f{\"u}r Europ{\"a}ische Wirtschaftsforschung}, abstract = {Computing power continues to grow at an enormous rate. Simultaneously, more and better data is increasingly available and Machine Learning methods have seen significant breakthroughs in the recent past. All this pushes further the boundary of what machines can do. Nowadays increasingly complex tasks are automatable at a precision which seemed infeasible only few years ago. The examples range from voice and image recognition, playing Go, to self-driving vehicles. Machines are able to perform more and more manual and also cognitive tasks that previously only humans could do. As a result of these developments, some argue that large shares of jobs are "at risk of automation", spurring public fears of massive job-losses and technological unemployment. This chapter discusses how new digital technologies might affect the labor market in the near future. First, the chapter discusses estimates of automation potentials, showing that many estimates are severely upward biased because they ignore that workers in seemingly automatable occupations already take over hard-to-automate tasks. Secondly, it highlights that these numbers only refer to what theoretically could be automated and that this must not be equated with job-losses or employment effects - a mistake that is done often in the public debate. Thirdly, the chapter develops scenarios on how digitalization is likely to affect the German labor market in the next five years and derives implications for policy makers on how to shape the future of work. Germany is an interesting case to study, as it is a developed country at the technological frontier. In particular, the main challenge will not be the number, but the structure of jobs and the corresponding need for supply side adjustments to meet the shift in demand both within and between occupations and sectors.}, keywords = {automation, Digitalization, Inequality, unemployment}, url = {http://hdl.handle.net/10419/200063}, author = {Melanie Arntz and Terry Gregory and Ulrich Zierahn} } @proceedings {2019, title = {The governance of artificial intelligence}, year = {2019}, month = {05/2019}, keywords = {AI use cases}, url = {https://www.bankofengland.co.uk/-/media/boe/files/speech/2019/managing-machines-the-governance-of-artificial-intelligence-speech-by-james-proudman}, author = {James Proudman} } @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} } @article {2019, title = {In the age of the smart artificial intelligence: AI{\textquoteright}s dual capacities for automating and informating work}, journal = {Business Information Review}, year = {2019}, month = {Dec-10-2020}, pages = {026638211988399}, publisher = {Sage}, abstract = {Recent developments in artificial intelligence (AI) have generated tidal waves, which are shaking the foundation of organizations and businesses. Even though AI is considered an unprecedented disruptive force for work automation, much can be learned from current research on the computerization of work. Drawing on the seminal work of Shoshana Zuboff, this article provides a balanced perspective on the dual affordances of AI systems for automating and informating work. Whereas AI offers unique capacities for automating cognitive work that once required high-skill workers, it may be a source of unintended consequences such as cognitive complacency or de-skilling workers. To overcome such effects, the informating capacities of AI systems can be invoked to augment work, generate a more comprehensive perspective on organization, and finally equip workers with new sets of intellectual skills. }, keywords = {Algorithmic management, artificial intelligence, augmentation, automating, informating, intelligent machine}, issn = {0266-3821}, doi = {10.1177/0266382119883999}, url = {https://journals.sagepub.com/doi/abs/10.1177/0266382119883999}, attachments = {https://waim.network/sites/crowston.syr.edu/files/In\%20the\%20Age\%20of\%20the\%20Smart\%20Artificial\%20Intelligence\%20AI\%E2\%80\%99s\%20Dual\%20Capacities\%20for\%20Automating\%20and\%20Informating.pdf}, author = {Jarrahi, Mohammad Hossein} } @article {2019, title = {Will artificial intelligence take over human resources recruitment and selection?}, journal = {Network Intelligence Studies}, volume = {VII}, year = {2019}, keywords = {artificial intelligence, human resources information systems, recruitment and selection}, url = {https://ideas.repec.org/a/cmj/networ/y2019i13p21-30.html}, author = {Bilal Hmoud and Varallya Laszlo} } @article {2018, title = {AI is coming after highly skilled jobs, and it{\textquoteright}s meeting resistance}, journal = {venturebeat}, year = {2018}, keywords = {AI}, url = {https://venturebeat.com/2018/03/04/ai-is-coming-after-highly-skilled-jobs-and-its-meeting-resistance/}, author = {Sascha Eder} } @article {2018, title = {Artificial Intelligence: A Case Study on Risk Mitigation}, journal = {International Journal of Computer Science and Network Security}, volume = {18}, year = {2018}, month = {04/2018}, pages = {9-12}, abstract = {Artificial intelligence technologies are expanding at an extraordinary rate with wide range of applications from machine translation to medical image analysis. Many applications are being developed and there is no doubt that in the near future this will create substantial risk for humanity. In addition, Artificial intelligence is a dual use are of technology as it can be used toward useful of harmful ends, for example autonomous drones can be used to deliver packages faster and easier and on the other hand, they can be used to deliver explosives. This literature review research investigates different types of artificial intelligence risks and proposes ways to mitigate these risks.}, keywords = {AI application, artificial intelligence, Risk mitigation}, author = {Ahmed AL-Gindy} } @article {2018, title = {Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making}, journal = {Business Horizons}, volume = {61}, year = {2018}, publisher = {Elsevier}, edition = {586}, chapter = {577}, abstract = {Artificial intelligence (AI) has penetrated many organizational processes, resulting in a growing fear that smart machines will soon replace many humans in decision making. To provide a more proactive and pragmatic perspective, this article highlights the complementarity of humans and AI and examines how each can bring their own strength in organizational decision-making process typically characterized by uncertainty, complexity, and equivocality. WIth a greater computational information processing capacity and an analytical approach, AI can extend humans{\textquoteright} cognition when addressing complexity, whereas human can still offer a more holistic, intuitive approach in dealing with uncertainty and equivocality in organizational decision making. This premise mirrors the idea of intelligence augmentation, which states that AI systems should be designed with the intention of augmenting, not replacing, human contribution.}, keywords = {Analytical and intuitive decision making, artificial intelligence, Human augmentation, Human-machine symbiosis, Organization decision making}, doi = {10.1016/j.bushor.2018.03.007}, author = {Mohammad Hossein Jarrahi} } @article {2018, title = {Artificial Intelligence in Medicine and Radiation Oncology}, journal = {Cureus}, year = {2018}, month = {Jan-04-2019}, abstract = {Artificial Intelligence (AI) was reviewed with a focus on its potential applicability to radiation oncology. The improvement of process efficiencies and the prevention of errors were found to be the most significant contributions of AI to radiation oncology. It was found that the prevention of errors is most effective when data transfer processes were automated and operational decisions were based on logical or learned evaluations by the system. It was concluded that AI could greatly improve the efficiency and accuracy of radiation oncology operations.}, keywords = {artificial intelligence, big data, error analysis, error prevention, machine learning, process efficiency, process optimization, quality improvement, radiation oncology}, doi = {10.7759/cureus.2475}, url = {https://www.cureus.com/articles/11443-artificial-intelligence-in-medicine-and-radiation-oncology}, author = {Weidlich, Vincent and Weidlich, Georg A.} } @article {2018, title = {Artificial Intelligence Is Automating Hollywood}, year = {2018}, month = {07/2018}, keywords = {AI use cases}, url = {https://futurism.com/artificial-intelligence-automating-hollywood-art}, author = {Dan Robitzki} } @article {2018, title = {Artificial intelligence, jobs, inequality and productivity: Does aggregate demand matter}, number = {ISSN 1871-9872}, year = {2018}, publisher = {Maastricht University}, keywords = {artificial intelligence, economics of automation, growth theory, innovation, labour demand, productivity, technology}, url = {http://ftp.iza.org/dp12005.pdf}, author = {Thomas Gries and Wim Naude} } @article {2018, title = {Artificial intelligence, machine learning and the evolution of healthcare A bright future or cause for concern?}, journal = {British Journal of Radiology}, volume = {7}, year = {2018}, pages = {223-225}, type = {Editorial}, keywords = {artificial intelligence, deep learning, machine learning}, author = {L.D. Jones and D. Golan and S. A. Hanna and M. Ramachandran} } @article {2018, title = {Automation, taxes and transfers with international rivalry}, year = {2018}, publisher = {Australian National University}, keywords = {automation, global modelling, income distribution, taxes, transfers}, issn = {2206-0332}, url = {https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2018-09/44_2018_tyers_zhou.pdf}, author = {Rod Tyers and Yixiao Zhou} } @article {2018, title = {{\textquoteleft}The beginning of a wave{\textquoteright}: A.I. Tiptoes into the workplace}, journal = {The NewYork Times}, year = {2018}, keywords = {AI, impacts}, url = {https://www.nytimes.com/2018/08/05/technology/workplace-ai.html}, author = {Steve Lohr} } @article {2018, title = {Cognitive and technological aspects of e-learning in context of robotization}, journal = {Cognitive Science {\textendash} New Media {\textendash} Education}, volume = {3}, year = {2018}, month = {Mar-06-2020}, pages = {65}, abstract = {The development of e-learning education led to the emergence of two main problems of such form of education, which are the user-system interaction from the cognitive prospective, and the analysis of massive data received out of students{\textquoteright} activity. The development of artificial intelligence concepts and robotic process automation (RPA) tools, both problems might be solved in a more efficient way. The article presents the intelligent system of feedbacks, realized as JavaScript extension to Moodle platform, which intends to strength cognitive output of the preformed learning activity, creating an illusion of the trainer{\textquoteright}s presence and, therefore, contribute to the resolution of the first problem. The resolution of the second problem is proposed using artificial intelligence worker built in Blue Prism RPA platform, which performs validation of test questions upon strict criteria of selection. Such validation process allows to select the questions which are coherent with the index of complexity and the index of differentiation capacity.}, keywords = {artificial intelligence worker, Blue Prism automation, intelligent system of feedbacks, robotic process automation}, issn = {2543-506X}, doi = {10.12775/CSNME.2017.013}, url = {http://apcz.umk.pl/czasopisma/index.php/CSNME/article/view/CSNME.2017.013}, author = {Shvets, Anna and Shvets, Valentyna} } @article {2018, title = {The discourse approach to boundary identification and corpus construction for theory review articles}, journal = {Journal of the Association for Information Systems}, year = {2018}, keywords = {article identification, boundary identification, citation search, keyword search, literature review, machine learning, research review, review article}, url = {https://www.researchgate.net/publication/325215971_Understanding_the_Elephant_The_Discourse_Approach_to_Boundary_Identification_and_Corpus_Construction_for_Theory_Review_Articles}, author = {Kai R. Larsen and Dirik S. Hovorka and Alan R. Dennis and Jevin D. West} } @article {2018, title = {G.M says its driverless car could be in fleets by next year}, year = {2018}, month = {01/2018}, keywords = {autonomous vehicles}, url = {https://www.nytimes.com/2018/01/12/business/gm-driverless-car.html}, author = {Neal E. Boudette} } @article {2018, title = {How driverless cars could disrupt the real estate industry}, year = {2018}, keywords = {autonomous vehicles}, url = {https://www.forbes.com/sites/elyrazin/2018/03/11/how-driverless-cars-could-disrupt-the-real-estate-industry/$\#$1595cb8613c1}, author = {Ely Razin} } @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 = {iNaturalist as a tool to expand the research value of museum specimens}, journal = {Applications in Plant Sciences}, volume = {6}, year = {2018}, month = {Jan-11-2018}, pages = {e01193}, keywords = {AI use cases}, doi = {10.1002/aps3.1193}, author = {Heberling, J. Mason and Isaac, Bonnie L.} } @proceedings {2018, title = {A literature Analysis of Research on Artificial Intelligence in Management Information System (MIS)}, year = {2018}, abstract = {This article presents an overview of Artificial Intelligence (AI) research applied in the Management Information System field by analyzing the databases of the AIS journal basket list representing the major international journal on MIS. AN initial set of 438 published papers was identified based on our search criteria. From those, 74 were selected because of their appropriate fit with the research questions. They were organized into domains, type of research, data collection strategy and AI techniques. We found; (1) MIS Quarterly published 42\% of the papers; (2) 50\% of studies were on Information Systems applications, followed by 15\% on Knowledge Management; and (3) 7 AI techniques were presented. This paper provides an overview of applied AI in Management, highlights the most studies related topics and techniques and contributes to a research agenda on the subject.}, keywords = {artificial intelligence, Information Systems, Literature Analysis}, author = {Alexandre Moreira Nascimento and Maria Alexandra V. C. da Cunha and Fernando de Souza Meirelles and Eusebio Scornavacca and Vinicius Veloso de Melo} } @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 = {Occupational Classifications With A Machine Learning Approach}, year = {2018}, keywords = {administrative data, machine learning, occupational classifications, transaction data, UMETRICS}, url = {https://www.iza.org/publications/dp/11738/occupational-classifications-a-machine-learning-approach}, author = {Akina Ikudo and Julia Lane and Joseph Staudt and Bruce A. Weinberg} } @article {2018, title = {Promethean AI uses artificial intelligence to help artists fill out game worlds}, year = {2018}, keywords = {AI use cases}, url = {https://venturebeat.com/2018/07/18/promethean-ai-uses-artificial-intelligence-to-help-artists-fill-out-game-worlds/}, author = {Dean Takahashi} } @article {2018, title = {Robots and Organization Studies: Why Robots Might Not Want to Steal Your Job}, journal = {Organization Studies}, year = {2018}, month = {04/2018}, pages = {017084061876556}, edition = {2}, abstract = {A number of recent high-profile studies of robotics and artificial intelligence (or AI) in economics and sociology have predicted that many jobs will soon disappear due to automation, with few new ones replacing them. While techno-optimists and techno-pessimists contest whether a jobless future is a positive development or not, this paper points to the elephant in the room. Despite successive waves of computerization (including advanced machine learning), jobs have not disappeared. And probably won{\textquoteright}t in the near future. To explain why, some basic insights from organization studies can make a contribution. I propose the concept of {\textquoteright}bounded automation{\textquoteright} to demonstrate how organizational forces mould the application of technology in the employment sector. If work does not vanish in the age of AI, then poorly paid jobs will most certainly proliferate, I argue. Finally, a case is made for the scholarly community to engage with wider social justice concerns. This I term public organization studies.}, keywords = {artificial intelligence, bounded automation, neoliberalism, public organization studies, Robotics, unemployment, work}, issn = {0170-8406}, doi = {10.1177/0170840618765568}, url = {http://journals.sagepub.com/doi/10.1177/0170840618765568}, author = {Fleming, Peter} } @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} } @conference {2018, title = {Work that Enables Care: Understanding Tasks, Automation, and the National Health Service}, booktitle = {iConference}, year = {2018}, publisher = {Springer}, organization = {Springer}, abstract = {Automation of jobs is discussed as a threat to many job occupations, but in the UK healthcare sector many view technology and automation as a way to save a threatened system. However, existing quantitative models that rely on occupation-level measures of the likelihood of automation suggest that few healthcare occupations are susceptible to automation. In order to improve these quantitative models, we focus on the potential impacts of task-level automation on health work, using qualitative ethnographic research to understand the mundane information work in general practices. By understanding the detailed tasks and variations of information work, we are building a more complete and accurate understanding of how healthcare staff work and interact with technology and with each other, often mediated by technology.}, keywords = {automation, Ethnography, Primary care, Sociotechnical}, doi = {10.1007/978-3-319-78105-1_60}, author = {Matt WIllis and Eric T. Meyer} } @conference {2017, title = {Are We Safe Enough in the Future of Artificial Intelligence? A Discussion on Machine Ethics and Artificial Intelligence Safety}, booktitle = {Scientific Methods in Academic Research and Teaching International Conference}, year = {2017}, pages = {184-197}, abstract = {Nowadays, there is a serious anxiety on the existence of dangerous intelligent systems and it is not just a science-fiction idea of evil machines like the ones in well-known Terminator movie or any other movies including intelligent robots - machines threatening the existence of humankind. So, there is a great interest in some alternative research works under the topics of Machine Ethics, and Existential Risks. The objective of this study is to provide a general discussion about the expressed research topics and try to find some answers to the question of {\textquoteright}Are we safe enough in the future of Artificial Intelligence?{\textquoteright}. In detail, the discussion includes a comprehensive focus on {\textquoteright}dystopic{\textquoteright} scenarios, enables interested researchers to think about some {\textquoteright}moral dilemmas{\textquoteright} and family have some ethical outputs that are considerable for developing good intelligent systems. From a general perspective, the discussion taken here is a good opportunity to improve awareness on the mentioned, remarkable research topics associated with not only Artificial Intelligence but also many other natural and social sciences taking role in the humankind}, keywords = {artificial intelligence, artificial intelligence safety, future of artificial intelligence, machine ethics, machine learning}, author = {Utku K{\"o}se} } @article {2017, title = {How AI is streamlining marketing and sales}, year = {2017}, month = {06/2017}, keywords = {AI use cases}, url = {https://hbr.org/2017/06/how-ai-is-streamlining-marketing-and-sales}, author = {Brad Power} } @article {2017, title = {Seeing Like a Tesla: How Can We Anticipate Self-Driving Worlds?}, journal = {Glocalism}, volume = {3}, year = {2017}, abstract = {In the last five years, investment and innovation in self-driving cars has accelerated dramatically. Automotive autonomy, once seen as impossible, is now sold as inevitable. Much of the governance discussion has centred on risk: will the cars be safer than their human-controlled counterparts? As with conventional cars, harder long-term questions relate to the future worlds that self-driving technologies might enable or even demand. The vision of an autonomous vehicle {\textendash} able to navigate the world{\textquoteright}s complexity using only its sensors and processors {\textendash} on offer from companies like Tesla is intentionally misleading. So-called {\textquotedblleft}autonomous{\textquotedblright} vehicles will depend upon webs of social and technical connectivity. For their purported benefits to be realised, infrastructures that were designed around humans will need to be upgraded in order to become machine-readable. It is vital to anticipate the politics of self-driving worlds in order to avoid exacerbating the inequalities that have emerged around conventional cars. Rather than being dazzled by the Tesla view, policymakers should start seeing like a city, from multiple perspectives. Good governance for self-driving cars means democratising experimentation and creating genuine collaboration between companies and local governments. }, keywords = {automotive autonomy, governance, risk, self-driving cars, Tesla}, doi = {10.12893/gjcpi.2017.3.2}, author = {Jack Stilgoe} } @article {2017, title = { A self-driving shuttle in Las Vegas got into an accident on its first day of service}, year = {2017}, month = {11/2017}, keywords = {autonomous vehicles}, url = {https://www.theverge.com/2017/11/8/16626224/las-vegas-self-driving-shuttle-crash-accident-first-day}, author = {Nick Statt} } @article {2017, title = {Thinking critically about and researching algorithms}, journal = {Information, Communication \& Society}, volume = {20}, year = {2017}, month = {Feb-01-2017}, pages = {14 - 29}, keywords = {Algorithmic management}, issn = {1369-118X}, doi = {10.1080/1369118X.2016.1154087}, author = {Kitchin, Rob} } @article {2017, title = {Will Life Be Worth Living in a World Without Work? Technological Unemployment and the Meaning of Life}, journal = {Science and Engineering Ethics}, volume = {23}, year = {2017}, publisher = {Springer}, chapter = {41-64}, abstract = {Suppose we are about to enter an era of increasing technological unemployment. What implications does this have for society? Two distinct ethical/social issues would seem to arise. The first is one of distributive justice: how will the (presumed) efficiency gains from automated labour be distributed through society? The second is one of personal fulfillment and meaning: if people no longer have to work, what will they do with their lives? In this article, I set aside the first issue and focus on the second. In doing so, I make three arguments. First I argue that there are good reasons to embrace non-work and that these reasons become more compelling in an era of technological unemployment. Second, I argue that the technological advances that make widespread technological unemployment possible could still threaten or undermine human flourishing and meaning, especially if (as is to be expected) they do not remain confined to the economic sphere. And third, I argue that this threat could be contained if we adopt an integrative approach to our relationship with technology. In advancing these arguments, I draw on three distinct literatures: (1) the literature on technological unemployment and workplace automation; (2) the antiwork critique - which I argue gives reasons to embrace technological unemployment; and (3) the philosophical debate about the conditions for meaning in life - which I argue gives reasons for concern.}, keywords = {Antiwork, automation, Egalitarianism, Freedom, Meaning of life, Technological unemployment, Transhumanism}, doi = {10.1007s/11948-016-9770-5}, author = {John Danaher} } @article {2016, title = {AI can show us the ravages of climate change}, year = {2016}, keywords = {AI use cases}, url = {https://www.technologyreview.com/f/613547/ai-can-show-us-the-ravages-of-climate-change/}, author = {Will Knight} } @mastersthesis {2016, title = {Exploring design principles for human-machine symbiosis: Insights from constructing an air transportation logistics artifact}, year = {2016}, keywords = {air transportation logistics, design science research, heuristic theorizing, huma n - machine symbiosis}, url = {https://www.semanticscholar.org/paper/Artificial-Intelligence-and-its-Role-in-Near-Future-Shabbir-Anwer/b93d9995f9ce3b15f4c4855ae62f0bf6f9bc041f}, author = {Daniel A. D{\"o}ppner and Detlef Schoder and Robert Wayne Gregory and Honorata Siejka} } @article {2013, title = {A Comprehensive Survey of Data Mining-based Fraud Detection Research}, year = {2013}, keywords = {adversarial detection, automated fraud detection, Data mining applications}, url = {https://arxiv.org/ftp/arxiv/papers/1009/1009.6119.pdf}, author = {Clifton Phua and Vincent Lee and Kate Smith and Ross Gayler} }