Papers
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. (2024). ReelFramer: Human-AI Co-Creation for News-to-Video Translation. In CHI Conference on Human Factors in Computing Systems. Retrieved de https://arxiv.org/abs/2304.09653
. (2020). Artificial intelligence and the world of work, a co-constitutive relationship. Journal Of The Association For Information Science And Technology. https://doi.org/10.1002/asi.24388
. (2020). Artificial intelligence and the world of work, a co-constitutive relationship. Journal Of The Association For Information Science And Technology. https://doi.org/10.1002/asi.24388
. (2020). Feminist economic geography and the future of work. Epa: Economy And Space, 1–12. https://doi.org/10.1177/0308518X20947101
. (2020). The nature of the Artificially Intelligent Firm - An economic investigation into changes that AI brings to the firm. Telecommunications Policy, 44, 101954. https://doi.org/10.1016/j.telpol.2020.101954
. (2020). Robo-Apocalypse cancelled? Reframing the automation and future of work debate. In Journal of Information Technology. https://doi.org/10.1177/0268396220925830
. (2020). Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. 1–9. Retrieved de http://arxiv.org/abs/2004.07213
. (2020). Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. 1–9. Retrieved de http://arxiv.org/abs/2004.07213
. (2019). Algorithms at War: The Promise, Peril, and Limits of Artificial Intelligence. International Studies Review. https://doi.org/10.1093/isr/viz025
. (2019). Is an army of robots marching on Chinese jobs?. Iza – Institute Of Labor Economics, (IZA DP No. 12281). Retrieved de https://www.iza.org/publications/dp/12281/is-an-army-of-robots-marching-on-chinese-jobs
. (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). 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). 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). 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