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Dyuti Pandya European Centre for International Political Economy
Devesh Narayanan Stanford University
Bas Becker Vrije Universiteit Amsterdam
Luciana Dadderio Edinburgh University
Jocelyn Cranefield Victoria University of Wellington

Recent publications

The Role of Human Creativity in the Presence of AI Creativity Tools at Work: A Case Study on AI-Driven Content Transformation in Journalism , Sitong Wang; Jocelyn McKinnon-Crowley; Tao Long; Kian Loong Lua; Keren Henderson; Kevin Crowston; Jeffrey V. Nickerson; Mark Hansen; Lydia B. Chilton
Deskilling and upskilling with AI systems , Kevin Crowston; Francesco Bolici
Artificial Intelligence and the Future of Citizen Science , Lucy Fortson; Kevin Crowston; Laure Kloetzer; Marisa Ponti
Editorial: Sharing work with AI: introduction to the special issue on the futures of work in the age of intelligent machines , Kevin Crowston; Ingrid Erickson; Jeffrey Nickerson
Taming artificial intelligence: A theory of control–accountability alignment among AI developers and users , Gudela Grote; Sharon Parker; Kevin Crowston

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2019

Brinker, T. J., Hekler, A., Enk, A., Klode, J., Hauschild, A., Berking, C., et al.. (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

Brinker, T. J., Hekler, A., Enk, A., Klode, J., Hauschild, A., Berking, C., et al.. (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

Brinker, T. J., Hekler, A., Enk, A., Klode, J., Hauschild, A., Berking, C., et al.. (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

Brinker, T. J., Hekler, A., Enk, A., Klode, J., Hauschild, A., Berking, C., et al.. (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

Brinker, T. J., Hekler, A., Enk, A., Klode, J., Hauschild, A., Berking, C., et al.. (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

Brinker, T. J., Hekler, A., Enk, A., Klode, J., Hauschild, A., Berking, C., et al.. (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

Brinker, T. J., Hekler, A., Enk, A., Klode, J., Hauschild, A., Berking, C., et al.. (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

Brinker, T. J., Hekler, A., Enk, A., Klode, J., Hauschild, A., Berking, C., et al.. (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

Brinker, T. J., Hekler, A., Enk, A., Klode, J., Hauschild, A., Berking, C., et al.. (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

Brinker, T. J., Hekler, A., Enk, A., Klode, J., Hauschild, A., Berking, C., et al.. (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

Brinker, T. J., Hekler, A., Enk, A., Klode, J., Hauschild, A., Berking, C., et al.. (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

Brinker, T. J., Hekler, A., Enk, A., Klode, J., Hauschild, A., Berking, C., et al.. (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

Brinker, T. J., Hekler, A., Enk, A., Klode, J., Hauschild, A., Berking, C., et al.. (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

Brinker, T. J., Hekler, A., Enk, A., Klode, J., Hauschild, A., Berking, C., et al.. (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

Brinker, T. J., Hekler, A., Enk, A., Klode, J., Hauschild, A., Berking, C., et al.. (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

Brinker, T. J., Hekler, A., Enk, A., Klode, J., Hauschild, A., Berking, C., et al.. (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

Brinker, T. J., Hekler, A., Enk, A., Klode, J., Hauschild, A., Berking, C., et al.. (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

Gill, K. S. (2019). Designing AI futures: A symbiotic vision (Vol. 1083, pp. 3 - 18; A. G. Kravets, Groumpos, P. P., Shcherbakov, M., & Kultsova, M., Eds.). In (Vol. 1083, pp. 3 - 18). https://doi.org/10.1007/978-3-030-29743-5_1

Gill, K. S. (2019). Designing AI futures: A symbiotic vision (Vol. 1083, pp. 3 - 18; A. G. Kravets, Groumpos, P. P., Shcherbakov, M., & Kultsova, M., Eds.). In (Vol. 1083, pp. 3 - 18). https://doi.org/10.1007/978-3-030-29743-5_1

Goldfarb, A., & Tucker, C.. (2019). Digital Economics. Journal Of Economic Literature, 57(1), 3 - 43. https://doi.org/10.1257/jel.20171452

Arntz, M., Gregory, T., & Zierahn, U.. (2019). Digitalization and the future of work: Macroeconomic consequences. Retrieved de http://hdl.handle.net/10419/200063

Groshen, E. L. (2019). Is a driverless future also jobless?. https://doi.org/10.17848/pb2019-17

Gemmell, L., Wenham, L., & Hauert, S.. (2019). Educating those most impacted by artificial intelligence (Vol. 11626, pp. 344 - 349; S. Isotani, Millán, E., Ogan, A., Hastings, P., McLaren, B., & Luckin, R., Eds.). In (Vol. 11626, pp. 344 - 349). https://doi.org/10.1007/978-3-030-23207-8_63

Agrawal, A., Gans, J., & Goldfarb, A.. (2019). How artificial intelligence and machine learning can impact market design (pp. 567 - 586). In (pp. 567 - 586). https://doi.org/10.7208/chicago/9780226613475.003.0023

Agrawal, A., Gans, J., & Goldfarb, A.. (2019). How artificial intelligence and machine learning can impact market design (pp. 567 - 586). In (pp. 567 - 586). https://doi.org/10.7208/chicago/9780226613475.003.0023

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