Model AI Governance Framework for Generative AI May 2024 1 1 · Table (p.4)
From Model-AI-Governance-Framework-for-Generative-AI-May-2024-1-1.pdf · page 4
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| MODEL AI GOVERNANCE FRAMEWORK FOR GENERATIVE AI | |
| b) Data — Data is a core element of model development. It significantly impacts | |
| the quality of the model output. Hence, what is fed to the model is important | |
| and there is a need to ensure data quality, such as through the use of trusted | |
| data sources. In cases where the use of data for model training is potentially | |
| contentious, such as personal data and copyright material, it is also important | |
| to give business clarity, ensure fair treatment, and to do so in a pragmatic way. | |
| c) Trusted Development and Deployment — Model development, and the application | |
| deployment on top of it, are at the core of AI-driven innovation. Notwithstanding | |
| the limited visibility that end-users may have, meaningful transparency around | |
| the baseline safety and hygiene measures undertaken is key. This involves | |
| industry adopting best practices in development, evaluation, and thereafter | |
| “food label”-type transparency and disclosure. This can enhance broader | |
| awareness and safety over time. | |
| d) | Incident Reporting — Even with the most robust development processes and |
| safeguards, no software we use today is completely foolproof. The same | |
| applies to AI. Incident reporting is an established practice, and allows for timely | |
| notification and remediation. Establishing structures and processes to enable | |
| incident monitoring and reporting is therefore key. This also supports continuous | |
| improvement of AI systems. | |
| e) Testing and Assurance — For a trusted ecosystem, third-party testing and | |
| assurance plays a complementary role. We do this today in many domains, | |
| such as finance and healthcare, to enable independent verification. Although | |
| AI testing is an emerging field, it is valuable for companies to adopt third-party | |
| testing and assurance to demonstrate trust with their end-users. It is also | |
| important to develop common standards around AI testing to ensure quality | |
| and consistency. | |
| f) | Security — Generative AI introduces the potential for new threat vectors against |
| the models themselves. This goes beyond security risks inherent in any software | |
| stack. While this is a nascent area, existing frameworks for information security | |
| need to be adapted and new testing tools developed to address these risks. | |
| g) Content Provenance — AI-generated content, because of the ease with which | |
| it can be created, can exacerbate misinformation. Transparency about where | |
| and how content is generated enables end-users to determine how to consume | |
| online content in an informed manner. Governments are looking to technical | |
| solutions like digital watermarking and cryptographic provenance. These | |
| technologies need to be used in the right context. | |
| h) Safety and Alignment Research & Development (R&D) — The state-of-the- | |
| science today for model safety does not fully cover all risks. Accelerated | |
| investment in R&D is required to improve model alignment with human intention | |
| and values. Global cooperation among AI safety R&D institutes will be critical to | |
| optimise limited resources for maximum impact, and keep pace with commercially | |
| driven growth in model capabilities. | |
| i) | AI for Public Good — Responsible AI goes beyond risk mitigation. It is also about |
| uplifting and empowering our people and businesses to thrive in an AI-enabled | |
| future. Democratising AI access, improving public sector AI adoption, upskilling | |
| workers and developing AI systems sustainably will support efforts to steer AI | |
| towards the Public Good. |
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