Papers
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. (In Press). Taming artificial intelligence: A theory of control–accountability alignment among AI developers and users. Academy Of Management Review. https://doi.org/10.5465/amr.2023.0117
. (2020). Are machines stealing our jobs?. Cambridge Journal Of Regions, Economy And Society, 13, 153–173. https://doi.org/10.1093/cjres/rsz025
. (2020). Are machines stealing our jobs?. Cambridge Journal Of Regions, Economy And Society, 13, 153–173. https://doi.org/10.1093/cjres/rsz025
. (2020). Conceptualization of the Human-Machine Symbiosis – A Literature Review. In Proceedings of the 53rd Hawaii International Conference on System Sciences (3, 289–298). https://doi.org/10.24251/hicss.2020.036
. (2020). 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). Explainable AI under contract and tort law: legal incentives and technical challenges. Artificial Intelligence And Law. https://doi.org/10.1007/s10506-020-09260-6
. (2020). How the FDA regulates AI. Academic Radiology, 27(1), 58 - 61. https://doi.org/10.1016/j.acra.2019.09.017
. (2020). International organisations and the future of work: How new technologies and inequality shaped the narratives in 2019. In Journal of Industrial Relations (pp. 477–507). https://doi.org/10.1177/0022185620913129
. (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
. (2020). When machines think for us: The consequences for work and place. Cambridge Journal Of Regions, Economy And Society, 13, 3–23. https://doi.org/10.1093/cjres/rsaa004
. (2020). When machines think for us: The consequences for work and place. Cambridge Journal Of Regions, Economy And Society, 13, 3–23. https://doi.org/10.1093/cjres/rsaa004
. (2019). AI’s path to the present and the painful transitions along the way. Digital Policy, Regulation And Governance, 21(3), 305 - 321. https://doi.org/10.1108/DPRG-09-2018-0051
. (2019). AI-Based Digital Assistants. Business & Information Systems Engineering, 61(4), 535 - 544. https://doi.org/10.1007/s12599-019-00600-8
. (2019). The ambiguous labor market impact of automating prediction. https://doi.org/10.3386/w25619
. (2019). The ambiguous labor market impact of automating prediction. https://doi.org/10.3386/w25619
. (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 Deep Learning: The Future of Medicine and Medical Practice. Journal Of The Association Of Physicians Of India ■ Vol. 67 . Retrieved de https://www.semanticscholar.org/paper/Artificial-Intelligence-and-Deep-Learning%3A-The-of-Sanal-Paul/795dc72decfb12389bbf7076d82fef70421b9933
. (2019). Bots Coordinating Work in Open Source Software Projects. Computer, 52(9), 52 - 60. https://doi.org/10.1109/MC.2018.2885970
. (2019). Collaboration and delegation between humans and AI: An experimental investigation of the future of work. Ssrn Electronic Journal. https://doi.org/10.2139/ssrn.3368813
. (2019). Collaboration and delegation between humans and AI: An experimental investigation of the future of work. Ssrn Electronic Journal. https://doi.org/10.2139/ssrn.3368813
. (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 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