@article {9998,
	title = {Making sense of AI systems development},
	journal = {IEEE Transactions on Software Engineering},
	volume = {50},
	year = {2024},
	month = {12/2023},
	pages = {123{\textendash}140},
	abstract = {We identify and describe episodes of sensemaking around challenges in modern Artificial-Intelligence (AI)-based systems development that emerged in projects carried out by IBM and client companies. All projects used IBM Watson as the development platform for building tailored AI-based solutions to support workers or customers of the client companies. Yet, many of the projects turned out to be significantly more challenging than IBM and its clients had expected. The analysis reveals that project members struggled to establish reliable meanings about the technology, the project, context, and data to act upon. The project members report multiple aspects of the projects that they were not expecting to need to make sense of yet were problematic. Many issues bear upon the current-generation AI{\textquoteright}s inherent characteristics, such as dependency on large data sets and continuous improvement as more data becomes available. Those characteristics increase the complexity of the projects and call for balanced mindfulness to avoid unexpected problems.},
	doi = {10.1109/TSE.2023.3338857},
	attachments = {https://waim.network/sites/crowston.syr.edu/files/sensemaking_tse_to_share.pdf},
	author = {Mateusz Dolata and Kevin Crowston}
}
@conference {osterlund2024,
	title = {Mutual learning in human-AI interaction},
	booktitle = {Trust and Reliance in Evolving Human-AI Workflows (TREW) Workshop, ACM CHI Conference},
	year = {2024},
	address = {Honolulu, HI},
	abstract = {<p>We explore the bi-directional relationship between human and machine learning in citizen science. Theoretically, the study draws on the Zone of Proximal Development concept, which allows us to describe the augmentation of human learning by AI, human augmentation of machine learning and how tasks can be designed to facilitate co-augmentation. Methodologically, the study utilizes a design-science approach to explore the design, deployment, and evaluations of the Gravity Spy citizen science project. The findings highlight the challenges and opportunities of co-augmentation, where both humans and machines contribute to each other{\textquoteright}s learning and capabilities.\&nbsp; The research contributes to the existing literature by emphasizing the role of ZPD in citizen science projects, showcasing how the concept supports ongoing learning for volunteers and keeps machine learning aligned with evolving data.</p>},
	attachments = {https://waim.network/sites/crowston.syr.edu/files/TREW_Workshop_Paper.pdf},
	author = {{\O}sterlund, Carsten and Crowston, Kevin and Jackson, Casey B. and Takou-Ayaoh, Marlene and Wu, Yunan and Katsaggelos, Aggelos K.}
}
@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 {2019,
	title = {A machine is cheaper than a human for the same task},
	journal = {AI \& SOCIETY},
	year = {2019},
	month = {Feb-01-2019},
	issn = {0951-5666},
	doi = {10.1007/s00146-018-0874-0},
	author = {Pereira, Lu{\'\i}s Moniz}
}
@article {2019,
	title = {Machine learning concepts, concerns and opportunities for a pediatric radiologist},
	journal = {Pediatric Radiology},
	volume = {49},
	year = {2019},
	month = {Jan-04-2019},
	pages = {509 - 516},
	issn = {0301-0449},
	doi = {10.1007/s00247-018-4277-7},
	author = {Moore, Michael M. and Slonimsky, Einat and Long, Aaron D. and Sze, Raymond W. and Iyer, Ramesh S.}
}
@inbook {2019,
	title = {Machine learning for clinical psychology and clinical neuroscience},
	booktitle = {researchgate.net},
	year = {2019},
	abstract = {A rapid growth in computational power and an increasing availability of large, publicly-accessible, multimodal datasets present new opportunities for psychology and neuroscience researchers to ask novel questions, and to approach old questions in novel ways. Studies of the personal characteristics, situation-specific factors, and sociocultural contexts that result in the onset, development, maintenance, and remission of psychopathology, are particularly well-suited to benefit from machine learning methods. However, introductory textbooks for machine learning rarely tailor their guidance to the needs of psychology and neuroscience researchers. Similarly, the traditional statistical training of clinical scientists often does not incorporate these approaches. This chapter acts as an introduction to machine learning for researchers in the fields of clinical psychology and clinical neuroscience. We discuss these methods, illustrated through real and hypothetical applications in the fields of clinical psychology and clinical neuroscience. We touch on study design, selecting appropriate techniques, how (and how not) to interpret results, and more, to aid researchers who are interested in applying machine learning methods to clinical science data.},
	url = {https://www.researchgate.net/publication/331000572_Machine_Learning_for_Clinical_Psychology_and_Clinical_Neuroscience},
	author = {Marc N. Countanche and Lauren S. Hallion}
}
@conference {2019,
	title = {Machine learning in Artificial Intelligence: Towards a common understanding},
	booktitle = {Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences},
	year = {2019},
	publisher = {Hawaii International Conference on System Sciences},
	organization = {Hawaii International Conference on System Sciences},
	doi = {10.24251/HICSS.2019.630},
	author = {K{\"u}hl, Niklas and Goutier, Marc and Hirt, Robin and Satzger, Gerhard}
}
@article {2019,
	title = {A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic},
	journal = {JCO Clinical Cancer Informatics},
	year = {2019},
	month = {Jan-04-2019},
	pages = {1 - 17},
	issn = {2473-4276},
	doi = {10.1200/CCI.18.00056},
	author = {Salvucci, Manuela and Rahman, Arman and Resler, Alexa J. and Udupi, Girish M. and McNamara, Deborah A. and Kay, Elaine W. and Laurent-Puig, Pierre and Longley, Daniel B. and Johnston, Patrick G. and Lawler, Mark and Wilson, Richard and Salto-Tellez, Manuel and Van Schaeybroeck, Sandra and Rafferty, Mairin and Gallagher, William M. and Rehm, Markus and Prehn, Jochen H.M.}
}
@article {2019,
	title = {Managing Machines: The governance of artificial intelligence},
	year = {2019},
	url = {https://www.bankofengland.co.uk/speech/2019/james-proudman-speech-at-fca-conference-on-governance-in-banking-london},
	author = {James Proudman}
}
@article {2019,
	title = {Marketing AI recruitment: The next phase in job application and selection},
	journal = {Computers in Human Behavior},
	volume = {90},
	year = {2019},
	month = {Jan-01-2019},
	pages = {215 - 222},
	issn = {07475632},
	doi = {10.1016/j.chb.2018.09.009},
	author = {van Esch, Patrick and Black, J. Stewart and Ferolie, Joseph}
}
@article {2019,
	title = {Massive technological unemployment without redistribution: A case for cautious optimism},
	journal = {Science and Engineering Ethics},
	volume = {25},
	year = {2019},
	month = {Jan-10-2019},
	pages = {1389 - 1407},
	issn = {1353-3452},
	doi = {10.1007/s11948-018-0070-0},
	author = {Chomanski, Bartek}
}
@article {2019,
	title = {Mitigating bias in algorithmic employment screening: Evaluating claims and practices},
	year = {2019},
	abstract = {<p>There has been rapidly growing interest in the use of algorithms for employment assessment,especially as a means to address or mitigate bias in hiring. Yet, to date, little is known abouthow these methods are being used in practice. How are algorithmic assessments built, vali-dated, and examined for bias? In this work, we document and assess the claims and practicesof companies offering algorithms for employment assessment, using a methodology that can beapplied to evaluate similar applications and issues of bias in other domains. In particular, weidentify vendors of algorithmic pre-employment assessments (i.e., algorithms to screen candi-dates), document what they have disclosed about their development and validation procedures,and evaluate their techniques for detecting and mitigating bias. We find that companies{\textquoteright} for-mulation of {\textquotedblleft}bias{\textquotedblright} varies, as do their approaches to dealing with it. We also discuss the variouschoices vendors make regarding data collection and prediction targets, in light of the risks andtrade-offs that these choices pose. We consider the implications of these choices and we raise anumber of technical and legal considerations.</p>},
	keywords = {social power of algorithms},
	url = {https://www.researchgate.net/publication/333971698_Mitigating_Bias_in_Algorithmic_Employment_Screening_Evaluating_Claims_and_Practices},
	author = {Manish Raghavan and Solon Barocas and Jon KleinbergKaren Levy}
}
@article {2019,
	title = {The moderating roles of perceived organizational support and competitive psychological climate},
	journal = {Tourism Management},
	volume = {73},
	year = {2019},
	month = {Jan-08-2019},
	pages = {172 - 181},
	issn = {02615177},
	doi = {10.1016/j.tourman.2019.02.006},
	author = {Li, Jun (Justin) and Bonn, Mark A. and Ye, Ben Haobin}
}
@book {2019,
	title = {Moral reasoning at work automation and ethics},
	year = {2019},
	pages = {69 - 77},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	isbn = {978-3-030-15190-4},
	url = {10.1007/978-3-030-15191-1_8},
	author = {Kvalnes, {\O}yvind and Kvalnes, {\O}yvind}
}
@book {2018,
	title = {Machine intelligence: Blessing or curse? It depends on us!},
	year = {2018},
	pages = {25 - 39},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	isbn = {978-3-319-90868-7},
	doi = {10.1007/978-3-319-90869-4_4},
	author = {Helbing, Dirk},
	editor = {Helbing, Dirk}
}
@book {2018,
	title = {Machine Learning for Ecology and Sustainable Natural Resource ManagementUse of Machine Learning (ML) for Predicting and Analyzing Ecological and {\textquoteleft}Presence Only{\textquoteright} Data: An Overview of Applications and a Good Outlook},
	year = {2018},
	pages = {27 - 61},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	isbn = {978-3-319-96976-3},
	doi = {10.1007/978-3-319-96978-7_2},
	author = {Huettmann, Falk and Craig, Erica H. and Herrick, Keiko A. and Baltensperger, Andrew P. and Grant Humphries and Lieske, David J. and Miller, Katharine and Mullet, Timothy C. and Oppel, Steffen and Resendiz, Cynthia and Rutzen, Imme and Schmid, Moritz S. and Suwal, Madan K. and Young, Brian D.},
	editor = {Grant Humphries and Magness, Dawn R. and Huettmann, Falk}
}
@book {2018,
	title = {Machine Learning for Ecology and Sustainable Natural Resource ManagementMachine Learning and {\textquoteleft}The Cloud{\textquoteright} for Natural Resource Applications: Autonomous Online Robots Driving Sustainable Conservation Management Worldwide?},
	year = {2018},
	pages = {353 - 377},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	isbn = {978-3-319-96976-3},
	doi = {10.1007/978-3-319-96978-7_18},
	author = {Grant Humphries and Huettmann, Falk},
	editor = {Grant Humphries and Magness, Dawn R. and Huettmann, Falk}
}
@inbook {2018,
	title = {Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Science, Code Sharing, Metadata and a Brief Historical Perspective},
	booktitle = {Machine Learning for Ecology and Sustainable Natural Resource Management},
	year = {2018},
	pages = {3 - 26},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	isbn = {978-3-319-96976-3},
	doi = {10.1007/978-3-319-96978-7_1},
	author = {Grant Humphries and Huettmann, Falk},
	editor = {Grant Humphries and Magness, Dawn R. and Huettmann, Falk}
}
@article {2018,
	title = {Machine learning, social learning and the governance of self-driving cars},
	journal = {Social Studies of Science},
	volume = {48},
	year = {2018},
	pages = {25-56},
	abstract = {Self-driving cars, a quintessentially {\textquoteleft}smart{\textquoteright} technology, are not born smart. The algorithms that control their movements are learning as the technology emerges. Self-driving cars represent a high-stakes test of the powers of machine learning, as well as a test case for social learning in technology governance. Society is learning about the technology while the technology learns about society. Understanding and governing the politics of this technology means asking {\textquoteleft}Who is learning, what are they learning and how are they learning?{\textquoteright} Focusing on the successes and failures of social learning around the much-publicized crash of a Tesla Model S in 2016, I argue that trajectories and rhetorics of machine learning in transport pose a substantial governance challenge. {\textquoteleft}Self-driving{\textquoteright} or {\textquoteleft}autonomous{\textquoteright} cars are misnamed. As with other technologies, they are shaped by assumptions about social needs, solvable problems, and economic opportunities. Governing these technologies in the public interest means improving social learning by constructively engaging with the contingencies of machine learning.},
	doi = {10.1177/0306312717741687},
	author = {Jack Stilgoe}
}
@conference {2018,
	title = {Machines as Teammates: A Collaboration Research Agenda},
	booktitle = {Hawaii International Conference on System Sciences},
	year = {2018},
	publisher = {Hawaii International Conference on System Sciences},
	organization = {Hawaii International Conference on System Sciences},
	doi = {10.24251/HICSS.2018.055},
	url = {http://hdl.handle.net/10125/49887},
	attachments = {https://waim.network/sites/crowston.syr.edu/files/SeeberEtAl_2018_MachinesAsTeammates.pdf},
	author = {Seeber, Isabella and Bittner, Eva and Briggs, Robert O. and de Vreede, Gert-Jan and de Vreede, Triparna and Druckenmiller, Doug and Maier, Ronald and Merz, Alexander B. and Oeste-Rei{\ss}, Sarah and Randrup, Nils and Gerhard Schwabe and S{\"o}llner, Matthias}
}
@article {2018,
	title = {Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR)},
	journal = {Systematic Reviews},
	volume = {7},
	year = {2018},
	month = {Jan-12-2018},
	keywords = {science},
	doi = {10.1186/s13643-018-0740-7},
	author = {Elaine Beller and Justin Clark and Guy Tsafnat and Clive Adams and Heinz Diehl and Hans Lund and Mourad Ouzzani and Kristina Thayer and James Thomas and Tari Turner and Jun Xia and Karen Robinson and Paul Glasziou}
}
@article {2018,
	title = {Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR)},
	journal = {Systematic Reviews},
	year = {2018},
	month = {2018},
	publisher = {Springer},
	type = {Review},
	abstract = {Systematic reviews (SR) are vital to health care, but have become complicated and time-consuming, due to the rapid expansion of evidence to be synthesised. Fortunately, many tasks of systematic reviews have the potential to be automated or may be assisted by automation. Recent advances in natural language processing, text mining and machine learning have produced new algorithms that can accurately mimic human endeavour in systematic review activity, faster and more cheaply. Automation tools need to be able to work together, to exchange data and results. Therefore, we initiated the International Collaboration for the Automation of Systematic Reviews (ICASR), to successfully put all the parts of automation of systematic review production together. The first meeting was held in Vienna in October 2015. We established a set of principles to enable tools to be developed and integrated into toolkits.
This paper sets out the principles devised at that meeting, which cover the need for improvement in efficiency of SR tasks, automation across the spectrum of SR tasks, continuous improvement, adherence to high quality standards, flexibility of use and combining components, the need for a collaboration and varied skills, the desire for open source, shared code and evaluation, and a requirement for replicability through rigorous and open evaluation.
Automation has a great potential to improve the speed of systematic reviews. Considerable work is already being done on many of the steps involved in a review. The {\textquoteright}Vienna Principles{\textquoteright} set out in this paper aim to guide a more coordinated effort which will allow the integration of work by separate teams and build on the experience, code and evaluations done by the many teams working across the globe.},
	keywords = {automation, Collaboration, Systematic review},
	doi = {10.1186/s13643-018-0740-7},
	author = {Elaine Beller and Justin Clark and Guy Tsafnat and Clive Adams and Heinz Diehl and Hans Lund and Mourad Ouzzani and Kristina Thayer and James Thomas and Tari Turner and Jun Xia and Karen Robinson and Paul Glasziou}
}
@article {2018,
	title = {Making Sure We Don{\textquoteright}t Forget the Basics When Using Machine Learning},
	journal = {JNCI: Journal of the National Cancer Institute},
	volume = {111},
	year = {2018},
	month = {Sep-10-2019},
	pages = {529 - 530},
	issn = {0027-8874},
	doi = {10.1093/jnci/djy179},
	author = {Winn, Aaron N and Neuner, Joan M}
}
@article {2018,
	title = {Managing talented worker in the era of new psychological contract},
	journal = {Jurnal Aplikasi Manajemen},
	volume = {16},
	year = {2018},
	month = {Jan-03-2018},
	pages = {20 - 26},
	issn = {16935241},
	doi = {10.21776/ub.jam.2018.016.01.03},
	author = {Haryadi, Haryadi and Anggraeni, Ade Irma and Ibrahim, Daing Nasir}
}
@article {2018,
	title = {Medicine and the rise of the robots: a qualitative review of recent advances of artificial intelligence in health},
	journal = {BMJ Leader},
	volume = {2},
	year = {2018},
	month = {Apr-06-2020},
	pages = {59 - 63},
	doi = {10.1136/leader-2018-000071},
	author = {Loh, Erwin}
}
@article {2018,
	title = {A method to link advances in Artificial Intelligence to occupational abilities},
	journal = {AEA Papers and Proceedings},
	volume = {108},
	year = {2018},
	month = {Jan-01-2018},
	pages = {54 - 57},
	issn = {2574-0768},
	doi = {10.1257/pandp.20181021},
	author = {Felten, Edward W. and Raj, Manav and Seamans, Robert}
}
@article {2017,
	title = {Machine Learning to Detect Anomalies from Application Logs},
	year = {2017},
	url = {https://www.druva.com/blog/machine-learning-detect-anomalies-application-logs/},
	author = {Adwait Bhave}
}
@article {2017,
	title = {Meet the people who train the robots to do their own jobs},
	year = {2017},
	url = {https://nyti.ms/2paCS5X},
	author = {Daisuke Wakabayashi}
}
@article {2016,
	title = {Machine learning and decision support in critical care},
	journal = {Proceedings of the IEEE},
	volume = {104},
	year = {2016},
	month = {Jan-02-2016},
	pages = {444 - 466},
	issn = {0018-9219},
	doi = {10.1109/JPROC.2015.2501978},
	author = {Johnson, Alistair E. W. and Ghassemi, Mohammad M. and Nemati, Shamim and Niehaus, Katherine E. and Clifton, David and Clifford, Gari D.}
}
@article {2016,
	title = {Models and people: An alternative view of the emergent properties of computational models},
	journal = {Complexity},
	volume = {21},
	year = {2016},
	month = {Jan-07-2016},
	pages = {202 - 213},
	doi = {10.1002/cplx.21680},
	author = {Boschetti, Fabio}
}
@article {RN30392,
	title = {The moral imperative of artificial intelligence},
	journal = {Communications of the ACM},
	volume = {59},
	year = {2016},
	pages = {5},
	type = {Journal Article},
	issn = {0001-0782},
	doi = {10.1145/2903530},
	author = {Vardi, Moshe Y.}
}
@book {2015,
	title = {Mining programming activity to promote help},
	year = {2015},
	pages = {23 - 42},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	isbn = {978-3-319-20498-7},
	doi = {10.1007/978-3-319-20499-4_2},
	author = {Carter, Jason and Dewan, Prasun},
	editor = {Boulus-R{\o}dje, Nina and Ellingsen, Gunnar and Bratteteig, Tone and Aanestad, Margunn and Bj{\o}rn, Pernille}
}
