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Does AI qualify for the job? A bidirectional model mapping labour and AI intensities. In AIES 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (94–100). https://doi.org/10.1145/3375627.3375831
. (2020). Similarity Learning Networks for Animal Individual Re-Identification-Beyond the Capabilities of a Human Observer. In Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2020 (479, 44–52). https://doi.org/10.1109/WACVW50321.2020.9096925
. (2020). Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation. The Tqm Journal, ahead-of-p. https://doi.org/10.1108/TQM-12-2019-0303
. (2020). Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation. The Tqm Journal, ahead-of-p. https://doi.org/10.1108/TQM-12-2019-0303
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Understanding the Impact of Artificial Intelligence on Services. In International Conference on Exploring Services Science (1, 202–213). https://doi.org/10.1007/978-3-030-38724-2
. (2020). Understanding the Impact of Artificial Intelligence on Services. In International Conference on Exploring Services Science (1, 202–213). https://doi.org/10.1007/978-3-030-38724-2
. (2020). Algorithms, artificial intelligence and automated decisions concerning workers and the risks of discrimination: the necessary collective governance of data protection. Transfer: European Review Of Labour And Research, 25(4), 465 - 481. https://doi.org/10.1177/1024258919876416
. (2019). Artificial Intelligence and the Future of the Drug Safety Professional. Drug Safety, 42(4), 491 - 497. https://doi.org/10.1007/s40264-018-0746-z
. (2019). A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European Journal Of Cancer, 111, 148 - 154. https://doi.org/10.1016/j.ejca.2019.02.005
. (2019). A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European Journal Of Cancer, 111, 148 - 154. https://doi.org/10.1016/j.ejca.2019.02.005
. (2019). A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European Journal Of Cancer, 111, 148 - 154. https://doi.org/10.1016/j.ejca.2019.02.005
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Digital Economics. Journal Of Economic Literature, 57(1), 3 - 43. https://doi.org/10.1257/jel.20171452
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How to Streamline AI Application in Government? A Case Study on Citizen Participation in Germany ( ). In Electronic Government (pp. 233-247). https://doi.org/10.1007/978-3-030-27325-5
. (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
. (2019). Recruiters just wanna have AI? (Spring semester 2019 ed.). In Department of Management and Engineering (Spring semester 2019 ed.). Retrieved de https://liu.diva-portal.org/smash/get/diva2:1333711/FULLTEXT01.pdf
. (2019). Why small data holds the key to the future of artificial intelligence. In 8th International Conference on Data Science, Technology and ApplicationsProceedings of the 8th International Conference on Data Science, Technology and Applications (340 - 347). https://doi.org/10.5220/0007956203400347
. (2019). Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review. Journal Of Affective Disorders, 241, 519-532. https://doi.org/10.1016/j.jad.2018.08.073
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