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Boundary spanning at the science–policy interface: the practitioners’ perspectives. Sustainability Science, 13(4), 1175 - 1183. https://doi.org/10.1007/s11625-018-0550-9
. (2018). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), 5 - 14. https://doi.org/10.1177/0008125619864925
. (2019). Can artificial intelligence make work more human?. Strategic Hr Review, 17(5), 263 - 264. https://doi.org/10.1108/SHR-10-2018-146
. (2018). The changing face of digital science ( ). In the 27th international conference extended abstractsProceedings of the 27th international conference extended abstracts on Human factors in computing systems - CHI EA '09 (4819). https://doi.org/10.1145/1520340.1520749
. (2009). The changing face of digital science ( ). In the 27th international conference extended abstractsProceedings of the 27th international conference extended abstracts on Human factors in computing systems - CHI EA '09 (4819). https://doi.org/10.1145/1520340.1520749
. (2009). Cities of the Future? The Potential Impact of Artificial Intelligence. Ai, 1, 192–197. https://doi.org/10.3390/ai1020012
. (2020). Comment on “Should we fear the robot revolution? (The correct answer is yes)” by Andrew Berg, Ed Buffie, and Felipe Zanna. Journal Of Monetary Economics, 97, 149 - 152. https://doi.org/10.1016/j.jmoneco.2018.05.012
. (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). 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). 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 comparison between human–human online conversations and human–chatbot conversations. Computers In Human Behavior, 49, 245 - 250. https://doi.org/10.1016/j.chb.2015.02.026
. (2015). Computational power and the social impact of artificial intelligence. Ssrn Electronic Journal. https://doi.org/10.2139/ssrn.3147971
. (2018). Consider the human work experience when integrating robotics in the workplace. In 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (75 - 84). https://doi.org/10.1109/HRI.2019.8673139
. (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). 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).