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Dyuti Pandya European Centre for International Political Economy
Devesh Narayanan Stanford University
Bas Becker Vrije Universiteit Amsterdam
Luciana Dadderio Edinburgh University
Jocelyn Cranefield Victoria University of Wellington

Recent publications

Taming artificial intelligence: A theory of control–accountability alignment among AI developers and users , Gudela Grote; Sharon Parker; Kevin Crowston
Supporting and augmenting human and machine learning in citizen science: Lessons from Gravity Spy , Carsten Østerlund; Kevin Crowston; Corey B. Jackson; Yunan Wu; Alexander O. Smith; Aggelos K. Katsaggelos
Deskilling and upskilling with generative AI systems , Kevin Crowston; Francesco Bolici
The task matters: The effect of perceived similarity to AI on intention to use in different task types , Qingyu Liang; Kevin Crowston; Sangseok You
ReelFramer: Human-AI Co-Creation for News-to-Video Translation , Sitong Wang; Samia Menon; Tao Long; Keren Henderson; Dingzeyu Li; Kevin Crowston; Mark Hansen; Jeffrey V. Nickerson; Lydia B. Chilton

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2019

von Wolff, R. Meyer, Hobert, S., & Schumann, M.. (2019). How May I Help You? – State of the Art and Open Research Questions for Chatbots at the Digital Workplace (T. Bui, Tran.). In Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences. Presented at the Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2019.013

Samuel, S. (2019). How one scientist coped when AI beat him at his life’s work. Vox. Retrieved de https://www.vox.com/future-perfect/2019/2/15/18226493/deepmind-alphafold-artificial-intelligence-protein-folding

Mcmahon, M., Mumper, D., Ihaza, M., & Farrar, D.. (2019). How Smart is your Manufacturing? Build Smarter with AI. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC)2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC) (55 - 60). https://doi.org/10.1109/COMPSAC.2019.10183

Balta, D., Kuhn, P., Sellami, M., Kulus, D., Lieven, C., & Krcmar, H.. (2019). How to Streamline AI Application in Government? A Case Study on Citizen Participation in Germany (I. Lindgren, Janssen, M., Lee, H., Polini, A., Bolívar, M. Pedro Rodr, Scholl, H. Jochen, & Tambouris, E., Eds.). In Electronic Government (pp. 233-247). https://doi.org/10.1007/978-3-030-27325-5

Tursunbayeva, A. (2019). Human resource technology disruptions and their implications for human resources management in healthcare organizations. Bmc Health Services Research, 19(1). https://doi.org/10.1186/s12913-019-4068-3

Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. Marco. (2019). Hybrid Intelligence. Business & Information Systems Engineering, 61(5), 637 - 643. https://doi.org/10.1007/s12599-019-00595-2

Zhang, H., Feinzi, S., Raisbec, L., McCombe, I., Guenol, N., Montalt, J., & Messe, K.. (2019). IBM Talent Management Solutions The role of AI in mitigating bias to enhance diversity and inclusion. Presented at the. Retrieved de https://www.ibm.com/downloads/cas/2DZELQ4O

Wilson, L., & Flynn, P. Mac. (2019). The impact of automation technologies for employment in Northern Ireland. Retrieved de https://www.nerinstitute.net/research/the-future-of-work-the-impact-of-automation-techno-1/

Parry, E., & Battista, V.. (2019). The impact of emerging technologies on work: a review of the evidence and implications for the human resource function. Emerald Open Research, 1, 5. https://doi.org/10.12688/emeraldopenres.12907.1

Reis, J., Santo, P. Espírito, Lisbon, P., & Melão, N.. (2019). Impacts of Artificial Intelligence on Public Administration: A Systematic Literature Review. In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI). Presented at the 2019 14th Iberian Conference on Information Systems and Technologies (CISTI). Retrieved de https://ieeexplore.ieee.org/document/8760778

Crowston, K., & Bolici, F.. (2019). Impacts of machine learning on work. In Proceedings of the 52nd Hawai'i International Conference on System Sciences (HICSS-52). https://doi.org/10.24251/HICSS.2019.719

Impacts_of_machine_learning_on_work__revision_.pdf (300.76 KB)

Jarrahi, M. Hossein. (2019). In the age of the smart artificial intelligence: AI’s dual capacities for automating and informating work. Business Information Review, 026638211988399. https://doi.org/10.1177/0266382119883999

In the Age of the Smart Artificial Intelligence AI’s Dual Capacities for Automating and Informating.pdf (317.68 KB)

Duckworth, P., Graham, L., & Osborne, M.. (2019). Inferring work task automatability from AI expert evidence (V. Conitzer, Hadfield, G., & Vallor, S., Trans.). In the 2019 AAAI/ACM ConferenceProceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society - AIES '19 (485 - 491). https://doi.org/10.1145/330661810.1145/3306618.3314247

Korinek, A. (2019). Integrating ethical values and economic value to steer progress in Artificial Intelligenc. In National bureau of economic research. Retrieved de https://www.nber.org/papers/w26130

Choi, D. Y., & Kang, J. Hyeung. (2019). Introduction: The Future of Jobs in an Increasingly Autonomous Economy. Journal Of Management Inquiry, 28(3), 298 - 299. https://doi.org/10.1177/1056492619827373

Cirillo, V., & Zayas, J. Molero. (2019). Labor, technology and work organization: An introduction to the forum. Journal Of Industrial And Business Economics, 46(3), 313 - 321. https://doi.org/10.1007/s40812-019-00126-w

Pereira, L. Moniz. (2019). A machine is cheaper than a human for the same task. Ai & Society. https://doi.org/10.1007/s00146-018-0874-0

Moore, M. M., Slonimsky, E., Long, A. D., Sze, R. W., & Iyer, R. S.. (2019). Machine learning concepts, concerns and opportunities for a pediatric radiologist. Pediatric Radiology, 49(4), 509 - 516. https://doi.org/10.1007/s00247-018-4277-7

Countanche, M. N., & Hallion, L. S.. (2019). Machine learning for clinical psychology and clinical neuroscience. In researchgate.net. Retrieved de https://www.researchgate.net/publication/331000572_Machine_Learning_for_Clinical_Psychology_and_Clinical_Neuroscience

Kühl, N., Goutier, M., Hirt, R., & Satzger, G.. (2019). Machine learning in Artificial Intelligence: Towards a common understanding (T. Bui, Tran.). In Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences. Presented at the Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2019.630

Salvucci, M., Rahman, A., Resler, A. J., Udupi, G. M., McNamara, D. A., Kay, E. W., et al.. (2019). A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic. Jco Clinical Cancer Informatics, (3), 1 - 17. https://doi.org/10.1200/CCI.18.00056

Proudman, J. (2019). Managing Machines: The governance of artificial intelligence. In FCA Conference on Governance in Banking. Retrieved de https://www.bankofengland.co.uk/speech/2019/james-proudman-speech-at-fca-conference-on-governance-in-banking-london

van Esch, P., J. Black, S., & Ferolie, J.. (2019). Marketing AI recruitment: The next phase in job application and selection. Computers In Human Behavior, 90, 215 - 222. https://doi.org/10.1016/j.chb.2018.09.009

Chomanski, B. (2019). Massive technological unemployment without redistribution: A case for cautious optimism. Science And Engineering Ethics, 25(5), 1389 - 1407. https://doi.org/10.1007/s11948-018-0070-0

Raghavan, M., Barocas, S., & Levy, J. KleinbergK.. (2019). Mitigating bias in algorithmic employment screening: Evaluating claims and practices. Retrieved de https://www.researchgate.net/publication/333971698_Mitigating_Bias_in_Algorithmic_Employment_Screening_Evaluating_Claims_and_Practices

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