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Artificial Intelligence Does Not Exist: Lessons from Shared Cognition and the Opposition to the Nature/Nurture Divide (Vol. 537, pp. 359 - 373; ). In (Vol. 537, pp. 359 - 373). https://doi.org/10.1007/978-3-319-99605-9_27
. (2018). Artificial Intelligence Does Not Exist: Lessons from Shared Cognition and the Opposition to the Nature/Nurture Divide (Vol. 537, pp. 359 - 373; ). In (Vol. 537, pp. 359 - 373). https://doi.org/10.1007/978-3-319-99605-9_27
. (2018). Mining programming activity to promote help (pp. 23 - 42; ). In (pp. 23 - 42). https://doi.org/10.1007/978-3-319-20499-4_2
. (2015). The social consequences of the digital revolution (Vol. 6). In (Vol. 6). https://doi.org/10.30687/2610-968910.30687/978-88-6969-273-410.30687/978-88-6969-273-4/008
. (2018). The future of human-AI collaboration: A taxonomy of design knowledge for hybrid intelligence systems. In Hawaii International Conference on System Sciences (HICSS). Presented at the Hawaii International Conference on System Sciences (HICSS). Retrieved de https://www.alexandria.unisg.ch/publications/254994
. (2019). Intelligible Models for HealthCare ( ). In the 21th ACM SIGKDD International ConferenceProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15 (1721 - 1730). https://doi.org/10.1145/278325810.1145/2783258.2788613
. (2015). SPIE ProceedingsBuilding a framework to manage trust in automation. In SPIE Defense + SecurityMicro- and Nanotechnology Sensors, Systems, and Applications IX (10194, 101941U). https://doi.org/10.1117/12.2264245
. (2017). . (2018).
Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration. Philosophical Transactions Of The Royal Society A: Mathematical, Physical And Engineering Sciences, 376(2128), 20170357. https://doi.org/10.1098/rsta.2017.0357
. (2018). Automating the black art: Creative places for artificial intelligence in audio mastering. Geoforum, 96, 77-86. https://doi.org/10.1016/j.geoforum.2018.08.005
. (2018). Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark. European Journal Of Cancer, 111, 30 - 37. https://doi.org/10.1016/j.ejca.2018.12.016
. (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). 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). 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). . (2017).