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AI Transformation
The document discusses challenges and directions for responsible AI. It outlines three gaps: 1) the need to align AI principles and standards with engineering practices; 2) the difficulty understanding inscrutable AI models; and 3) the misalignment between AI principles and system-level behaviors. It proposes closing these gaps through engineering practices, operationalizable frameworks, and connected design patterns. It also advocates understanding AI systems through testing and accountability measures. Finally, it discusses designing foundation model-based systems through capabilities rather than functions and ensuring tools are optimized for and trusted by humans and AI agents alike.
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AI Transformation
- 1. Australia’s National ScienceAgency AI Transformation - in highly regulated Industry Dr/Prof Liming Zhu, Research Director Expert in working groups • ISO/IEC JTC 1/WG 13 Trustworthiness • ISO/IEC JTC 1/SC 42/WG 3 – AI Trustworthiness
- 2. ”Directional” Combinatorial Innovation AI/MLCyber Human Distributed Trust Trustworthy AI/Data Ecosystem Human-Centred Cybersecurity Trustworthy/Federated AI/Agents/Identity Responsible AI Sovereign Identity & Transaction Trustworthy AI Practices & Supply Chains Trusted Collaborative Intelligence Trustworthy Digital Infrastructure
- 3. Generative AI •Text •Image/video •Code/Scripts •Data Predictive Diagnostic Concepts Clarified 3| Prescriptive Discriminative Model Generative Model Hybrid
- 4. (Generative) AI Transformation- Business 4 | • AI as Generic Capability/Digital Expert • human resources or tools/functionality • Blurring/Moving Division Boundaries • reverse Conway's Law on org & strategy • AI Supply Chain • Where is your AI? Who, How trained? Risk-based approach & experimentation
- 5. Australia’s National ScienceAgency Challenges
- 6. Australia’s AI ethicsframework OECD AI principles Principles Standards Frameworks NIST AI RMF ISO Standards Algorithms Models Data61’s SE for RAI …… … 1. The Vertical Gap – Alignment & Practices Model Alignment != System Alignment Principles/Standards != Eng. Practices Lu, Q., Luo, Y., Zhu, L., Tang, M., Xu, X., Whittle, J., 2023. Operationalising Responsible AI Using a Pattern-Oriented Approach: A Case Study on Chatbots in Financial Services. IEEE Intelligent Systems. 6 |
- 7. 2. The UnderstandingGap - Inscrutable Do we have to fully understand the AI model? Can system-level understanding help? 7 |
- 8. One More Thing– Here Come the LLMs 8 | Lu, Q., Zhu, L., Xu, X., Xing, Z., Whittle, J., 2023. Towards Responsible AI in the Era of ChatGPT: A Reference Architecture for Designing Foundation Model-based AI Systems. https://arxiv.org/abs/2304.11090
- 9. Australia’s National ScienceAgency Directions & Questions
- 10. 1. Close theGaps – engineering practices 10 | 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 (CAIN) Measurements/Metrics, Evaluation/Verification/Validation Methods
- 11. Close the Gaps– operationalisable 11 | Xia, B., Lu, Q., Perera, H., Zhu, L., Xing, Z., Liu, Y., Whittle, J., 2023. Towards Concrete and Connected AI Risk Assessment (C2AIRA). 2nd International Conference on AI Engineering (CAIN) Dozens of Frameworks Which methods & tools for which stakeholders?
- 12. Close the Gaps– Connected Patterns 12 | Lu, Q., Zhu, L., Xu, X., Whittle, J., 2023. Responsible-AI-by-Design: A Pattern Collection for Designing Responsible AI Systems. IEEE Software https://research.csiro.au/ss/science/projects/responsible-ai-pattern-catalogue/ Lee, S.U., Perera, H., Xia, B., Liu, Y., Lu, Q., Zhu, L., Salvado, O., Whittle, J., 2023. QB4AIRA: A Question Bank for AI Risk Assessment. https://doi.org/10.48550/arXiv.2305.09300
- 13. 2. Understand atthe System Level Increasingly, the study of these trained (but un-designed) systems seems destined to become a kind of natural science… … they are similar to the grand goals of biology, which is to "figure out" while being content to get by without proofs or guarantees … “AI as (an Ersatz) Natural Science?” by Subbarao Kambhampati 13 |
- 14. Understanding via “Testing” Zhuo,T.Y., Huang, Y., Chen, C., Xing, Z., 2023. Exploring AI Ethics of ChatGPT: A Diagnostic Analysis https://arxiv.org/abs/2301.12867 14 | ICSE23 DeepTest Keynote - Testing Generative Large Language Model: Mission Impossible or Where Lies the Path? Zhenchang Xing, CSIRO’s Data61 Capability +/-/⊥ Alignment Waluigi Effect prevents model-level solution
- 15. Understanding via Accountability 15| No Agreed Best Practices No Agreed Safety Test Verifiable investment in safety Accountability enforced by law/market
- 16. Understanding via Accountability 16| Xu, X., Wang, C., Wang, Jeff, Lu, Q., Zhu, L., 2022. Dependency tracking for risk mitigation in machine learning systems, in: 44th ICSE 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, in: 45th ICSE Software Bills of Materials (SBOM)/AIBOM
- 17. 3. Design FoundationModel-based Systems | Lu, Q., Zhu, L., Xu, X., Xing, Z., Whittle, J., 2023. A Framework for Designing Foundation Model based Systems https://arxiv.org/abs/2305.05352v1 LLM eating the traditional system functions Moving boundaries ex emerging capabilities • Design with capabilities, not functionalities • Design for capability evolution and agility Tools being optimized for LLM/Agents • Selected/Used by both human and LLM/Agents • Trusted by human and LLM/Agents
- 18. (Generative) AI Transformation- Infrastructure 18 | “Talk to Your Data” --> FM-based Innovation/Operational excellence “Zero-gradient Infrastructure” (few AI models) --> FM tuning and FMOps Lu, Q., Zhu, L., Xu, X., Xing, Z., Whittle, J., 2023. A Framework for Designing Foundation Model based Systems https://arxiv.org/abs/2305.05352v1
- 19. Responsible AI forLLM-based Applications 19 | Lu, Q., Zhu, L., Xu, X., Xing, Z., Whittle, J., 2023. Towards Responsible AI in the Era of ChatGPT: A Reference Architecture for Designing Foundation Model-based AI Systems. http://arxiv.org/abs/2304.11090
- 20. RAI in theEra of Foundation Models AI Engineering Directions • (Learned) Guardrails • Radical observability • Understand rather than build Responsible AI Engineering • Close the principle-alg. gaps • Engineering practices/methods • Measurement/metrics • Connected patterns • Understand at the system level • AIBOM & accountability More info & Contact https://research.csiro.au/ss/ Liming.Zhu@data61.csiro.au Brendan.Omalley@data61.csiro.au Coming out late 2023 Foundation Models • Design with capabilities, not func. • Design for system evolution • Tools optimised for LLM/Agents • Special RAI patterns Collaborate with CSIRO’s Data61 on • RAI Engineering best practices & evaluation • LLM/Foundation model-based system design/eval For the latest, follow me on Twitter: @limingz LinkedIn: Liming Zhu