AI Maintenance: A Robustness Perspective
Open accessAbstract
In this article, we carve out artificial intelligence (AI) maintenance from the robustness perspective. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle, which is an essential milestone toward building sustainable and trustworthy AI ecosystems.
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Several levels of robustness can be distinguished in AI-based systems.
“Systematically, several levels of robustness can be distinguished in AI-based systems [30]:”
From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [30]
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Model Cards for Model Reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681 · see citing works
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676 · 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
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
FactSheets: Increasing trust in AI services through supplier's declarations of conformity
Arnold, M., Bellamy, R. K. E., Hind, M., Houde, S., Mehta, S., Mojsilović, A., Nair, R., Ramamurthy, K. N., Olteanu, A., Piorkowski, D., et al. (2019) · IBM Journal of Research and Development · doi:10.1147/jrd.2019.2942288 · Cited by 434 · see citing works
Vision Transformers Are Robust Learners
Paul, S., & Chen, P.-Y. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20103 · Cited by 213 · see citing works
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Research works printed but not linked: 13
- P.-Y. Chen and S. Liu, "Holistic adversarial robustness of deep learning models," Proceedings of the AAAI Conference on Artificial Intelligence, 2023.
- R. S. S. Kumar, M. Nystr öm, J. Lambert, A. Marshall, M. Goertzel, A. Comissoneru, M. Swann, and S. Xia, "Adversarial machine learning-industry perspectives," in 2020 IEEE Security and Privacy Workshops (SPW), 2020, pp. 69-75.
- T. Gu, K. Liu, B. Dolan-Gavitt, and S. Garg, "BadNets: Evaluating backdooring attacks on deep neural net- works," IEEE Access, vol. 7, pp. 47 230-47 244, 2019.
- C. Xie, K. Huang, P.-Y. Chen, and B. Li, "DBA: Dis- tributed backdoor attacks against federated learning," in International Conference on Learning Representations, 2020.
- I. J. Goodfellow, J. Shlens, and C. Szegedy, "Explain- ing and harnessing adversarial examples," International Conference on Learning Representations, 2015.
- P.-Y. Chen, H. Zhang, Y. Sharma, J. Yi, and C.-J. Hsieh, "ZOO: Zeroth order optimization based black-box at- tacks to deep neural networks without training substitute models," in ACM Workshop on Artificial Intelligence and Security, 2017, pp. 15-26.
- T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Ka- plan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, "Language models are few-shot learners," in Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 1877-1901.
- R. Wang, G. Zhang, S. Liu, P.-Y. Chen, J. Xiong, and M. Wang, "Practical detection of trojan neural networks: Data-limited and data-free cases," in European Confer- ence on Computer Vision, 2020, pp. 222-238.
- Z. Yang, B. Li, P.-Y. Chen, and D. Song, "Characteriz- ing audio adversarial examples using temporal depen- dency," International Conference on Learning Repre- sentations, 2019.
- J. Yang, K. Zhou, Y. Li, and Z. Liu, "Generalized out-of-distribution detection: A survey," arXiv preprint arXiv:2110.11334, 2021. · arXiv
- M. Cheng, P.-Y. Chen, S. Liu, S. Chang, C.-J. Hsieh, and P. Das, "Self-progressing robust training," Proceedings of the AAAI Conference on Artificial Intelligence, 2021.
- A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, "Towards deep learning models resistant to adversarial attacks," International Conference on Learn- ing Representations, 2018.
- P.-Y. Chen and C.-J. Hsieh, Adversarial Robustness for Machine Learning. Elsevier, 2022.
Cited by
Sorted by citations
Díaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., López de Prado, M., Herrera-Viedma, E., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101896 · Cited by 800 · see citing works
- Authors
- Chen, P.-Y., & Das, P.
- Affiliations
- IBM Research - Thomas J. Watson Research Center (US)
- IBM (United States) (US)
- Venue
- Computer
- Published
- 2023-02
- Volume
- 56
- Issue
- 2
- Pages
- 48-56
- ISSN
- 0018-9162 (ISSN-L)
- 1558-0814
- OpenAlex
- W4319879104
- Cited by
- 29
- References
- 7
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 9 · 2023: 5
- FWCI
- 7.27
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Computer (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Keywords
- robustness, robustness analysis, AI lifecycle, AI regulation, trustworthy AI, status tracking
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