It was my absolute pleasure to deliver the opening talk at the AI workshop at the Australian Federal Police, where many senior executives attended. This talk differed from my usual presentations on AI engineering and responsible AI. I focused on the evolving nature of AI and how the roles of human and expert knowledge are changing. Different types of human expertise are required in the face of growing AI capabilities, unsupervised learning, and meta-learning.
This new wave of AI also possesses special qualities that necessitate a different approach from organizations, compared to earlier AIs and other emerging technologies.
Thanks for the invitation and I hope I earned the cute ‘Constable Kenny’. :-}
References
Zhu, L., Xu, X., Lu, Q., Governatori, G., Whittle, J., 2022. AI and Ethics—Operationalizing Responsible AI, in: Chen, F., Zhou, J. (Eds.), Humanity Driven AI. Springer International Publishing, Cham, pp. 15–33. https://doi.org/10.1007/978-3-030-72188-6_2
Lu, Q., Zhu, L., Xu, X., Whittle, J., Xing, Z., 2022. Towards a Roadmap on Software Engineering for Responsible AI, in: 1st International Conference on AI Engineering – Software Engineering for AI (CAIN). https://dl.acm.org/doi/abs/10.1145/3522664.3528607
Lu, Q., 2023. Towards Responsible AI in the Era of ChatGPT: Pattern-Oriented Reference Architecture for Designing Foundation Model based AI Systems. https://arxiv.org/abs/2304.11090
Lu, Q., Zhu, L., Xu, X., Xing, Z., Whittle, J., 2023. A Framework for Designing Foundation Model based Systems URL https://arxiv.org/abs/2305.05352v1
Lo, S.K., Liu, Y., Lu, Q., Wang, C., Xu, X., Paik, H.-Y., Zhu, L., 2023. Toward Trustworthy AI: Blockchain-Based Architecture Design for Accountability and Fairness of Federated Learning Systems. IEEE Internet of Things Journal 10, 3276–3284. https://doi.org/10.1109/JIOT.2022.3144450
Lo, S.K., Liu, Y., Lu, Q., Wang, C., Xu, X., Paik, H.-Y., Zhu, L., 2021. Blockchain-based Trustworthy Federated Learning Architecture. https://doi.org/10.48550/arXiv.2108.06912
Xia, B., Bi, T., Xing, Z., Lu, Q., Zhu, L., 2023. An Empirical Study on Software Bill of Materials: Where We Stand and the Road Ahead. Presented at the ICSE, arXiv. https://doi.org/10.48550/arXiv.2301.05362
Xu, X., Wang, C., Wang, Z. (Jef), Lu, Q., Zhu, L., 2022. Dependency tracking for risk mitigation in machine learning (ML) systems, in: Proceedings of the 44th International Conference on Software Engineering: Software Engineering in Practice,https://doi.org/10.1145/3510457.3513058
