ICSE 2023 Keynote – Software Engineering as the Linchpin of Responsible AI

Title: Software Engineering as the Linchpin of Responsible AI

Abstract: From humanity’s existential risks to safety risks in critical systems to ethical risks, responsible AI, as the saviour, has become a massive research challenge with significant real-world consequences. However, achieving responsible AI remains elusive despite the plethora of high-level ethical principles, risk frameworks and progress in algorithmic assurance. In the meantime, software engineering (SE) is being upended by AI, grappling with building system-level quality and alignment from inscrutable ML models and code generated from natural language prompts. The upending poses new challenges and opportunities for engineering AI systems responsibly.

This talk will share our experiences in helping the industry achieve responsible AI systems by inventing new SE approaches. It will dive into industry challenges (such as risk silos and principle-algorithm gaps) and research challenges (such as lack of requirements, emerging properties and inscrutable systems) and make the point that SE is the linchpin of responsible AI. But SE also requires some fundamental rethinking – shifting from building functions to AI systems to discovering and managing emerging functions from AI systems. Only by doing so can SE take on critical new roles, from understanding human intelligence to building a thriving human-AI symbiosis.

Time: 2023.03.18 (Thu) 9:30-10:30

Venue: MCEC, Melbourne, Australia

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


About Me

Research Director, CSIRO’s Data61
Conjoint Professor, CSE UNSW

For other roles, see LinkedIn & Professional activities.

If you’d like to invite me to give a talk, please see here & email liming.zhu@data61.csiro.au

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