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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

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

Situating “explainability”: Making sense of data-driven assemblages in organizational context , Presentation , Christine T. Wolf
Are We Ready For A Life Interdisciplinary , Presentation , Ryan Watkins
HSS 408-461: Robots + Work , Syllabus , Bernadette Long

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2019

Reis, J., Santo, P. Espírito, Lisbon, P., & Melão, N.. (2019). Impacts of Artificial Intelligence on Public Administration: A Systematic Literature Review. In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI). Presented at the 2019 14th Iberian Conference on Information Systems and Technologies (CISTI). Retrieved de https://ieeexplore.ieee.org/document/8760778

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

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

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

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

Raghavan, M., Barocas, S., & Levy, J. KleinbergK.. (2019). Mitigating bias in algorithmic employment screening: Evaluating claims and practices. Retrieved de https://www.researchgate.net/publication/333971698_Mitigating_Bias_in_Algorithmic_Employment_Screening_Evaluating_Claims_and_Practices

Sarwar, S., Dent, A., Faust, K., Richer, M., Djuric, U., Van Ommeren, R., & Diamandis, P.. (2019). Physician perspectives on integration of artificial intelligence into diagnostic pathology. Npj Digital Medicine, 2(1). https://doi.org/10.1038/s41746-019-0106-0

Metcalf, L., Askay, D. A., & Rosenberg, L. B.. (2019). Pooling knowledge through artificial swarm intelligence to improve business decision making. California Management Review, 61(4), 84 - 109. https://doi.org/10.1177/0008125619862256

Raj, M., & Seamans, R.. (2019). Primer on artificial intelligence and robotics. Journal Of Organization Design, 8(1). https://doi.org/10.1186/s41469-019-0050-0

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

Hagerty, A., & Rubinov, I.. (2019). A Review of the Social Impacts and Ethical Implications of Artificial Intelligence. Retrieved de https://arxiv.org/ftp/arxiv/papers/1907/1907.07892.pdf

Dahya, R., & Morris, A.. (2019). Toward a conceptual framework for understanding AI action and legal reaction (Vol. 11489, pp. 453 - 459; M. - J. Meurs & Rudzicz, F., Eds.). In (Vol. 11489, pp. 453 - 459). https://doi.org/10.1007/978-3-030-18305-9_44

Frank, M. R., Autor, D., Bessen, J. E., Brynjolfsson, E., Cebrian, M., Deming, D. J., et al.. (2019). Toward understanding the impact of artificial intelligence on labor. Proceedings Of The National Academy Of Sciences, 116, 6531–6539. https://doi.org/10.1073/pnas.1900949116

2018

Jokisch, O., & Huber, M.. (2018). Advances in the development of a cognitive user interface. Matec Web Of Conferences, 161, 01003. https://doi.org/10.1051/matecconf/201816101003

Jylkäs, T. T. Jylkastta, Äijälä, M., Vuorikari, T., & Rajab, V.. (2018). AI Assistants as Non-human Actors in Service Design. In 21st DMI: Academic Design Management Conference. Presented at the 21st DMI: Academic Design Management Conference. Retrieved de https://www.researchgate.net/publication/326997609_AI_Assistants_as_Non-human_Actors_in_Service_Design?enrichId=rgreq-80a84e881501993f8b87a3d6387900aa-XXX&enrichSource=Y292ZXJQYWdlOzMyNjk5NzYwOTtBUzo2NTkyMjU4MjI5MTY2MTBAMTUzNDE4MzA5Mjk2MQ%3D%3D&el=1_x_2&_

Garvey, C. (2018). AI risk mitigation through democratic governance (J. Furman, Marchant, G., Price, H., & Rossi, F., Trans.). In the 2018 AAAI/ACM ConferenceProceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society - AIES '18 (366 - 367). https://doi.org/10.1145/3278721.3278801

Lee, Y., Ragguett, R. - M., Mansur, R. B., Boutilier, J. J., Rosenblat, J. D., Trevizol, A., et al.. (2018). Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review. Journal Of Affective Disorders, 241, 519-532. https://doi.org/10.1016/j.jad.2018.08.073

Lee, Y., Ragguett, R. - M., Mansur, R. B., Boutilier, J. J., Rosenblat, J. D., Trevizol, A., et al.. (2018). Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review. Journal Of Affective Disorders, 241, 519-532. https://doi.org/10.1016/j.jad.2018.08.073

Lee, Y., Ragguett, R. - M., Mansur, R. B., Boutilier, J. J., Rosenblat, J. D., Trevizol, A., et al.. (2018). Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review. Journal Of Affective Disorders, 241, 519-532. https://doi.org/10.1016/j.jad.2018.08.073

Iklé, M., Franz, A., Rzepka, R., & Goertzel, B.. (2018). Artificial General Intelligence. Presented at the. Retrieved de https://ivizlab.org/wp-content/uploads/sites/2/2018/09/TurnerDiPaola_2018_ArtificialGeneralIntelligence.pdf

Huang, M. - H., & Rust, R. T.. (2018). Artificial Intelligence in Service. Journal Of Service Research, 21(2), 155 - 172. https://doi.org/10.1177/1094670517752459

Robitzki, D. (2018). Artificial Intelligence Is Automating Hollywood. Retrieved de Futurism website: https://futurism.com/artificial-intelligence-automating-hollywood-art

Jones, L. D., Golan, D., Hanna, S. A., & Ramachandran, M.. (2018). Artificial intelligence, machine learning and the evolution of healthcare A bright future or cause for concern?. British Journal Of Radiology, 7(7), 223-225.

Jones, L. D., Golan, D., Hanna, S. A., & Ramachandran, M.. (2018). Artificial intelligence, machine learning and the evolution of healthcare. Bone & Joint Research, 7(3), 223 - 225. https://doi.org/10.1302/2046-3758.73.BJR-2017-0147.R1

Bednarek, A. T., Wyborn, C., Cvitanovic, C., Meyer, R., Colvin, R. M., Addison, P. F. E., et al.. (2018). Boundary spanning at the science–policy interface: the practitioners’ perspectives. Sustainability Science, 13(4), 1175 - 1183. https://doi.org/10.1007/s11625-018-0550-9

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