Regulating AI: The Future of Foundation Models

Regulating AI: The Future of Foundation Models

Table of Contents:

1. Introduction
2. The Need for Foundation Models
3. Definition of Foundation Models
4. Development and Training of Foundation Models
5. Obligations for Providers of Foundation Models 5.1. Identification and Mitigation of Risks 5.2. Data Governance Measures 5.3. Performance and Efficiency 5.4. Technical Documentation and Quality Management 5.5. Registration in EU Database
6. Obligations for Foundation Models in Generative AI Systems
7. Compliance Challenges for Providers
8. Conclusion

Introduction

In this article, we will explore the concept of foundation models within the Context of the draft AI Act introduced by the EU Parliament. We will Delve into the need for foundation models and discuss their definition, development, and training processes. Furthermore, we will analyze the specific obligations imposed on providers of foundation models and address compliance challenges. By the end of this article, You will have a comprehensive understanding of foundation models and their significance in AI systems.

The Need for Foundation Models

As technological advancements Continue to outpace legislative frameworks, lawmakers are constantly striving to catch up. When the EU Commission drafted the AI Act in 2021, there was limited awareness of large language models and their potential. However, the emergence of ChatGPT transformed the AI landscape, prompting the EU Parliament to introduce foundation models as a new concept in the draft AI Act.

Definition of Foundation Models

Foundation models serve as the building blocks for other downstream AI systems. They are trained on broad data at Scale, designed for generality of output, and adaptable to a wide range of tasks. The AI Act's Article 3(1)(1c) provides a legal definition, while Recital (60 e) sheds more light on the development and training of foundation models. These models are developed from algorithms optimized for generality and versatility and undergo training on diverse data sources.

Development and Training of Foundation Models

Foundation models are trained on large amounts of data from a broad range of sources to accomplish various downstream tasks. They possess the capability to perform tasks beyond their intended purposes. AI systems, be it with specific intended purposes or general-purpose systems, can both be implementations of a foundation model. This means that a foundation model can be reused in numerous downstream AI or general-purpose systems.

Obligations for Providers of Foundation Models

The EU Parliament has included Article 28(b) in the draft AI Act, outlining several obligations for providers of foundation models. These obligations include the demonstration of risk identification, reduction, and mitigation in relation to health, safety, fundamental rights, the environment, and democracy. Providers must also adhere to appropriate data governance measures, assess data sources for biases, and employ suitable mitigation strategies. Additionally, they need to prioritize the performance, predictability, interpretability, corrigibility, safety, cybersecurity, energy efficiency, and resource management of foundation models. Extensive technical documentation, a quality management system, and registration in an EU database are also required.

Obligations for Foundation Models in Generative AI Systems

Providers of foundation models to be used in generative AI systems have additional obligations. These include designing and developing the models to ensure safeguards against content generation that violates Union law. Providers must also make a sufficiently detailed summary of training data usage, which is protected under copyright law, publicly available.

Compliance Challenges for Providers

Complying with the obligations imposed on providers of foundation models can be challenging. The identification and mitigation of risks, adherence to data governance measures, and meeting performance and efficiency standards require substantial effort. Technical documentation, quality management, and registration processes add further complexity. Moreover, fulfilling obligations in the context of generative AI systems, while safeguarding fundamental rights, poses additional challenges.

Conclusion

Foundation models play a crucial role in the development of AI systems and have specific obligations under the draft AI Act. Providers must ensure compliance with various requirements related to risk mitigation, data governance, performance, efficiency, and legal safeguards. However, meeting these obligations presents significant challenges. As AI legislation continues to evolve, striking the right balance between innovation and accountability remains essential.