@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}
}
@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}
}
@article {Zavrsnik2020,
	title = {Criminal justice, artificial intelligence systems, and human rights},
	journal = {ERA Forum},
	volume = {20},
	number = {4},
	year = {2020},
	pages = {567{\textendash}583},
	publisher = {The Author(s)},
	abstract = {The automation brought about by big data analytics, machine learning and artificial intelligence systems challenges us to reconsider fundamental questions of criminal justice. The article outlines the automation which has taken place in the criminal justice domain and answers the question of what is being automated and who is being replaced thereby. It then analyses encounters between artificial intelligence systems and the law, by considering case law and by analysing some of the human rights affected. The article concludes by offering some thoughts on proposed solutions for remedying the risks posed by artificial intelligence systems in the criminal justice domain.},
	keywords = {Algorithms, artificial intelligence, automation, Criminal justice, Fair trial, Human rights},
	isbn = {1202702000602},
	issn = {18639038},
	doi = {10.1007/s12027-020-00602-0},
	url = {http://dx.doi.org/10.1007/s12027-020-00602-0},
	author = {Zavr{\v s}nik, Ale{\v s}}
}
@article {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}
}
@booklet {Suran2020,
	title = {Frameworks for collective intelligence: A systematic literature review},
	howpublished = {ACM Computing Surveys},
	volume = {53},
	number = {1},
	year = {2020},
	pages = {1{\textendash}36},
	abstract = {Over the last few years, Collective Intelligence (CI) platforms have become a vital resource for learning, problem solving, decision-making, and predictions. This rising interest in the topic has to led to the development of several models and frameworks available in published literature. Unfortunately, most of these models are built around domain-specific requirements, i.e., they are often based on the intuitions of their domain experts and developers. This has created a gap in our knowledge in the theoretical foundations of CI systems and models, in general. In this article, we attempt to fill this gap by conducting a systematic review of CI models and frameworks, identified from a collection of 9,418 scholarly articles published since 2000. Eventually, we contribute by aggregating the available knowledge from 12 CI models into one novel framework and present a generic model that describes CI systems irrespective of their domains. We add to the previously available CI models by providing a more granular view of how different components of CI systems interact. We evaluate the proposed model by examining it with respect to six popular, ongoing CI initiatives available on the Web.},
	keywords = {collective intelligence, Crowdsourcing, Human computer interaction, Systematic literature review, Web 2.0, Wisdom of crowds},
	issn = {15577341},
	doi = {10.1145/3368986},
	author = {Suran, Shweta and Pattanaik, Vishwajeet and Draheim, Dirk}
}
@article {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 = {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 = {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 = {How cheap labor drives China{\textquoteright}s AI Ambitions},
	year = {2018},
	month = {11/2018},
	keywords = {human and machine intelligence},
	url = {https://www.nytimes.com/2018/11/25/business/china-artificial-intelligence-labeling.html},
	author = {Li Yuan}
}
@article {2018,
	title = {Human and smart machine co-learning with brain computer interface},
	journal = {IEEE SMC Magazine},
	volume = {4},
	year = {2018},
	keywords = {human and machine inttelligence},
	url = {https://arxiv.org/abs/1802.06521},
	author = {Chang - Shing Lee and Mei - Hui Wang and Li - Wei Ko and Naoyuki Kubota and Lu - An Lin and Shinya Kitaoka and Yu - Te Wang and Shun - Feng Su}
}
@conference {2018,
	title = {The impact of artificial intelligence on the HR function},
	booktitle = {IES Perspectives on HR 2018},
	year = {2018},
	keywords = {HR},
	url = {https://www.employment-studies.co.uk/system/files/resources/files/mp142_The_impact_of_Artificial_Intelligence_on_the_HR_function-Peter_Reilly.pdf},
	author = {Peter Reilly}
}
@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 {2016,
	title = {Human Interaction With Robot Swarms: A Survey},
	journal = {Transaction on human-machine systems},
	volume = {46},
	year = {2016},
	month = {02/2016},
	pages = {9-26},
	publisher = {IEEE},
	abstract = {Recent advances in technology are delivering robots of reduced size and cost. A natural outgrowth of these advances are systems comprised of large numbers of robots that collaborate autonomously in diverse applications. Research on effective autonomous control of such systems, commonly called swarms, has increased dramatically in recent years and received attention from many domains, such as bioinspired robotics and control theory. These kinds of distributed systems present novel challenges for the effective integration of human supervisors, operators, and teammates that are only beginning to be addressed. This paper is the first survey of human-swarm interaction (HSI) and identifies the core concepts needed to design a human-swarm system. We first present the basics of swarm robotics. Then, we introduce HSI from the perspective of a human operator by discussing the cognitive complexity of solving tasks with swarm systems. Next, we introduce the interface between swarm and operator and identify challenges and solutions relating to human-swarm communication, state estimation and visualization, and human control of swarm. For the latter, we develop a taxonomy of control methods that enable operators to control swarms effectively. Finally, we synthesize the results to highlight remaining challenges, unanswered questions, and open problems for HSI, as well as how to address them in future works.},
	keywords = {Human-robot interaction (HRI), human-swarm interaction (HSI), multi-robot systems, swarm robotics}
}
@article {2014,
	title = {A transdisciplinary perspective on hedonomic sustainability design},
	year = {2014},
	keywords = {environmental efficacy, environmental sustainability, hedonomics, sustainable design, transdisciplinary research},
	doi = {10.1177\%2F1064804613516762},
	url = {https://journals.sagepub.com/doi/abs/10.1177/1064804613516762?journalCode=erga},
	author = {Stephen M. Fiore and Elizabeth Philips and Brittany C. Sellers}
}
