15 challenges for AI: Or what AI (currently) can’t do
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Most experts concur that strong AI is either not likely to happen in the near future, or is unlikely to happen at all.
“most experts concur that strong AI is either not likely to happen in the near future, or is unlikely to happen at all(Hagendorff & Wezel, 2019;Wang & Siau, 2019).”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Hagendorff & Wezel, 2019
Cites
The ethics of algorithms: Mapping the debate
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · Big Data & Society · doi:10.1177/2053951716679679 · Cited by 3,432 · cited 1× · see citing works
“These value assumptions should be accompanied by ethical reflections (Mittelstadt et al. 2016).”
BackgroundHow the machine “thinks”: Understanding opacity in machine learning algorithms
Burrell, J. (2016) · Big Data & Society · doi:10.1177/2053951715622512 · Cited by 3,196 · cited 2× · see citing works
“A far-reaching discourse on black box algorithms (Diako-poulos 2014; Pasquale 2015), as well as on the problem of non-transparency—especially in deep learning—(Ananny and Crawford 2017; Burrell 2016) takes place in the ML community and the wider public.”
BackgroundTrajectories of the Earth System in the Anthropocene
Steffen, W., Rockström, J., Richardson, K., Lenton, T. M., Folke, C., Liverman, D., Summerhayes, C. P., Barnosky, A. D., Cornell, S. E., Crucifix, M., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1810141115 · Cited by 3,139 · cited 1× · see citing works
“In view of current political and ecological developments, there is no guarantee that this infrastructure will maintain stable in the future (Steffen et al. 2018).”
BackgroundBarocas, S., & Selbst, A. D. (2014) · doi:10.2139/ssrn.2477899 · Cited by 3,048 · see citing works
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2018) · arXiv · doi:10.48550/arxiv.1803.09010 · Cited by 2,474
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., & Crawford, K. (2021) · Communications of the ACM · doi:10.1145/3458723 · Cited by 2,469 · see citing works
Haggerty, K. D., & Ericson, R. V. (2000) · The British Journal of Sociology · doi:10.1080/00071310020015280 · Cited by 2,244 · see citing works
Ananny, M., & Crawford, K. (2018) · New Media & Society · doi:10.1177/1461444816676645 · Cited by 1,836 · see citing works
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Jagielski, M., Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., & Li, B. (2018) · 2018 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2018.00057 · Cited by 861 · see citing works
Deep Learning: A Critical Appraisal
Marcus, G. (2018) · arXiv · doi:10.48550/arxiv.1801.00631 · Cited by 853 · see citing works
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., et al. (2018) · arXiv · doi:10.48550/arxiv.1802.07228 · Cited by 849 · see citing works
Why Teaching Ethics to AI Practitioners Is Important
Goldsmith, J., & Burton, E. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11139 · Cited by 60 · see citing works
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Algorithms as work designers: How algorithmic management influences the design of jobs
Parent-Rocheleau, X., & Parker, S. K. (2022) · Human Resource Management Review · doi:10.1016/j.hrmr.2021.100838 · Cited by 512 · see citing works
Bickman, L. (2020) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-020-01065-8 · Cited by 184 · see citing works
Svenningsson, N., & Faraon, M. (2019) · Proceedings of the 2019 2nd Artificial Intelligence and Cloud Computing Conference · doi:10.1145/3375959.3375973 · Cited by 56 · see citing works
How Does AI Improve Human Decision-Making? Evidence from the AI-Powered Go Program
Choi, S., Kim, N., Kim, J., & Kang, H. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3893835 · Cited by 30 · see citing works
Global justice and the use of AI in education: ethical and epistemic aspects
Vučković, A., & Sikimić, V. (2025) · AI & SOCIETY · doi:10.1007/s00146-024-02076-x · Cited by 22 · see citing works
- Authors
- Hagendorff, T., & Wezel, K.
- Affiliations
- University of Tübingen (DE)
- Corresponding author
- Hagendorff, Thilo
- Venue
- AI & SOCIETY
- Published
- 2019-03-28
- Volume
- 35
- Issue
- 2
- Pages
- 355-365
- ISSN
- 0951-5666 (ISSN-L)
- 1435-5655
- OpenAlex
- W2934428127
- Cited by
- 113
- References
- 12
- Citations by year
- 2026: 6 · 2025: 37 · 2024: 21 · 2023: 12 · 2022: 16 · 2021: 15 · 2020: 4 · 2019: 2
- FWCI
- 4.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- AI & Society (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- AI challenges, AI limitations, machine learning applications, epistemology of AI
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