Who's new

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

Recent publications

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
Project archetypes: A blessing and a curse for AI development , Mateusz Dolata; Kevin Crowston; Gerhard Schwabe

Recently added resources

WAIM Doctoral Research Fellowship CFP , Presentation , Kevin Crowston, Ingrid Erickson, Jeffrey Nickerson
Using Work System Theory, Facets of Work, and Dimensions of Smartness to Characterize Applications and Impacts of AI , Report , Steven Alter
Converging Design: AI, Jobs, Job Design and Organizations , Presentation , Discussion Group

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Book

Galanos, V. (2018). Artificial Intelligence Does Not Exist: Lessons from Shared Cognition and the Opposition to the Nature/Nurture Divide (Vol. 537, pp. 359 - 373; D. Kreps, Ess, C., Leenen, L., & Kimppa, K., Eds.). In (Vol. 537, pp. 359 - 373). https://doi.org/10.1007/978-3-319-99605-9_27

Galanos, V. (2018). Artificial Intelligence Does Not Exist: Lessons from Shared Cognition and the Opposition to the Nature/Nurture Divide (Vol. 537, pp. 359 - 373; D. Kreps, Ess, C., Leenen, L., & Kimppa, K., Eds.). In (Vol. 537, pp. 359 - 373). https://doi.org/10.1007/978-3-319-99605-9_27

Carter, J., & Dewan, P.. (2015). Mining programming activity to promote help (pp. 23 - 42; N. Boulus-Rødje, Ellingsen, G., Bratteteig, T., Aanestad, M., & Bjørn, P., Eds.). In (pp. 23 - 42). https://doi.org/10.1007/978-3-319-20499-4_2

Basso, P., Chiaretti, G., Krzywdzinski, M., Gerber, C., & Evers, M.. (2018). The social consequences of the digital revolution (Vol. 6). In (Vol. 6). https://doi.org/10.30687/2610-968910.30687/978-88-6969-273-410.30687/978-88-6969-273-4/008

Conference Paper

Dellermann, D., Calma, A., Lipusch, N., Weber, T., Weigel, S., & Ebel, P.. (2019). The future of human-AI collaboration: A taxonomy of design knowledge for hybrid intelligence systems. In Hawaii International Conference on System Sciences (HICSS). Presented at the Hawaii International Conference on System Sciences (HICSS). Retrieved de https://www.alexandria.unisg.ch/publications/254994

Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N.. (2015). Intelligible Models for HealthCare (L. Cao, Zhang, C., Joachims, T., Webb, G., Margineantu, D. D., & Williams, G., Trans.). In the 21th ACM SIGKDD International ConferenceProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15 (1721 - 1730). https://doi.org/10.1145/278325810.1145/2783258.2788613

Metcalfe, J. S., Marathe, A. R., Haynes, B., Paul, V. J., Gremillion, G. M., Drnec, K., et al.. (2017). SPIE ProceedingsBuilding a framework to manage trust in automation. In SPIE Defense + SecurityMicro- and Nanotechnology Sensors, Systems, and Applications IX (10194, 101941U). https://doi.org/10.1117/12.2264245

Journal

Grace, K., Salvatier, J., Dafoe, A., Zhang, B., & Evans, O.. (2018). When Will AI Exceed Human Performance? Evidence from AI Experts. Retrieved de https://jair.org/index.php/jair/article/download/11222/26431/

Journal Article

Eder, S. (2018). AI is coming after highly skilled jobs, and it’s meeting resistance. Venturebeat. Retrieved de https://venturebeat.com/2018/03/04/ai-is-coming-after-highly-skilled-jobs-and-its-meeting-resistance/

Mikhaylov, S. Jankin, Esteve, M., & Campion, A.. (2018). Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration. Philosophical Transactions Of The Royal Society A: Mathematical, Physical And Engineering Sciences, 376(2128), 20170357. https://doi.org/10.1098/rsta.2017.0357

Birtchnell, T., & Elliott, A.. (2018). Automating the black art: Creative places for artificial intelligence in audio mastering. Geoforum, 96, 77-86. https://doi.org/10.1016/j.geoforum.2018.08.005

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

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

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S.. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542, 115–118.

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