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Devesh Narayanan Stanford University
Bas Becker Vrije Universiteit Amsterdam
Luciana Dadderio Edinburgh University
Jocelyn Cranefield Victoria University of Wellington
Wayne Wang Fundação Getulio Vargas

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Mutual learning in human-AI interaction , Carsten Østerlund; Kevin Crowston; Casey B. Jackson; Marlene Takou-Ayaoh; Yunan Wu; Aggelos K. Katsaggelos
Technological Frames: Interpretations about the Futures of Work and Intelligent Machines on Social Media , Ayşe Öcal; Kevin Crowston
ReelFramer: Human-AI Co-Creation for News-to-Video Translation , Sitong Wang; Samia Menon; Tao Long; Keren Henderson; Dingzeyu Li; Kevin Crowston; Mark Hansen; Jeffrey V. Nickerson; Lydia B. Chilton
Making sense of AI systems development , Mateusz Dolata; Kevin Crowston
AngleKindling: Supporting Journalistic Angle Ideation with Large Language Models , Savvas Petridis; Nicholas Diakopoulos; Kevin Crowston; Mark Hansen; Keren Henderson; Stan Jastrzebski; Jefrey V. Nickerson; Lydia B. Chilton

Recently added resources

Work in The Age Of AI, Robots, and Algorithms , Syllabus , Diane Bailey
Comm493: Analytics and Artificial Intelligence for Business , Syllabus , Tracy Jenkin

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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). 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

Mbilini, S. N., le Roux, D. B., & Parry, D. A.. (2019). Does automation influence career decisions among South African students? (C. de Villiers & Smuts, H., Trans.). In the South African Institute of Computer Scientists and Information Technologists 2019Proceedings of the South African Institute of Computer Scientists and Information Technologists 2019 on ZZZ - SAICSIT '19 (1 - 10). https://doi.org/10.1145/335110810.1145/3351108.3351137

Kim, S. Yun. (2019). The fourth industrial revolution: Trends and impacts on the world of work (pp. 177 - 194; S. McGrath, Mulder, M., Papier, J., & Suart, R., Eds.). In (pp. 177 - 194). https://doi.org/10.1007/978-3-319-94532-3_115

Proudman, J. (2019). The governance of artificial intelligence. In FCA Conference on Governance in Banking. Retrieved de https://www.bankofengland.co.uk/-/media/boe/files/speech/2019/managing-machines-the-governance-of-artificial-intelligence-speech-by-james-proudman

Balta, D., Kuhn, P., Sellami, M., Kulus, D., Lieven, C., & Krcmar, H.. (2019). How to Streamline AI Application in Government? A Case Study on Citizen Participation in Germany (I. Lindgren, Janssen, M., Lee, H., Polini, A., Bolívar, M. Pedro Rodr, Scholl, H. Jochen, & Tambouris, E., Eds.). In Electronic Government (pp. 233-247). https://doi.org/10.1007/978-3-030-27325-5

Parry, E., & Battista, V.. (2019). The impact of emerging technologies on work: a review of the evidence and implications for the human resource function. Emerald Open Research, 1, 5. https://doi.org/10.12688/emeraldopenres.12907.1

Pereira, L. Moniz. (2019). A machine is cheaper than a human for the same task. Ai & Society. https://doi.org/10.1007/s00146-018-0874-0

Salvucci, M., Rahman, A., Resler, A. J., Udupi, G. M., McNamara, D. A., Kay, E. W., et al.. (2019). A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic. Jco Clinical Cancer Informatics, (3), 1 - 17. https://doi.org/10.1200/CCI.18.00056

Proudman, J. (2019). Managing Machines: The governance of artificial intelligence. In FCA Conference on Governance in Banking. Retrieved de https://www.bankofengland.co.uk/speech/2019/james-proudman-speech-at-fca-conference-on-governance-in-banking-london

Pugh, A. J. (2019). Precarious lives: Job insecurity and well-being in rich democracies. Social Forces, 97(4), e1 - e3. https://doi.org/10.1093/sf/soz022

Saba, L., Biswas, M., Kuppili, V., Godia, E. Cuadrado, Suri, H. S., Edla, D. Reddy, et al.. (2019). The present and future of deep learning in radiology. European Journal Of Radiology, 114, 14 - 24. https://doi.org/10.1016/j.ejrad.2019.02.038

Maddox, T. M., Rumsfeld, J. S., & Payne, P. R. O.. (2019). Questions for artificial intelligence in health care. Jama, 321(1), 31. https://doi.org/10.1001/jama.2018.18932

Correia, A., Jameel, S., Paredes, H., Fonseca, B., & Schneider, D.. (2019). Steps toward a scaffolding design framework (pp. 149 - 161; V. - J. Khan, Papangelis, K., Lykourentzou, I., & Markopoulos, P., Eds.). In (pp. 149 - 161). https://doi.org/10.1007/978-3-030-12334-5_5

Correia, A., Jameel, S., Paredes, H., Fonseca, B., & Schneider, D.. (2019). Steps toward a scaffolding design framework (pp. 149 - 161; V. - J. Khan, Papangelis, K., Lykourentzou, I., & Markopoulos, P., Eds.). In (pp. 149 - 161). https://doi.org/10.1007/978-3-030-12334-5_5

Pendleton-Jullian, A., & Lempert, R.. (2019). Technology and Work Worth Doing (Not Jobs) Post-AlphaGo: A World-Building Workshop on the Future of Artificial Intelligence. https://doi.org/10.7249/CF398

Pulkka, V. - V. (2019). “This time may be a little different” – exploring the Finnish view on the future of work. International Journal Of Sociology And Social Policy, 39(1/2), 22 - 37. https://doi.org/10.1108/IJSSP-05-2018-0070

Pettersen, L. (2019). Why artificial intelligence will not outsmart complex knowledge work. Work, Employment And Society, 33(6), 1058 - 1067. https://doi.org/10.1177/0950017018817489

Greco, C., Polonioli, A., & Tagliabue, J.. (2019). Why small data holds the key to the future of artificial intelligence. In 8th International Conference on Data Science, Technology and ApplicationsProceedings of the 8th International Conference on Data Science, Technology and Applications (340 - 347). https://doi.org/10.5220/0007956203400347

2020

Cebreros, A., Heffner-Rodríguez, A., Livas, R., & Puggioni, D.. (2020). Automation Technologies and Employment at Risk : The Case of Mexico. Banco de México.

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