A Conceptual Framework and Preliminary Proposals for AI Alignment and Safety in R&D

Preface

The present blog post serves as an overview of a research report I authored over the summer as part of the CHERI fellowship program, under the supervision of Patrick Levermore. In this project, I explore the complexities of AI alignment, with a specific focus on reinterpreting the Eliciting Latent Knowledge problem through the lens of the Comprehensive AI Services (CAIS) model. Furthermore, I delve into the model's applicability in ensuring R&D design safety and certification.

I preface this post by acknowledging my novice status in the field of AI safety research. As such, this work may contain both conceptual and technical errors. However, the primary objective of this project was not to present a flawless research paper or groundbreaking discoveries. Rather, it served as an educational journey into the realm of AI safety - a goal I believe has been met - and as a foundation for my future research. Much of this work synthesises and summarises existing research, somewhat limiting (but not eliminating) the novelty of my contributions. Nonetheless, I hope it can offer a fresh perspective on well-established problems.

You can find the complete report here.

I welcome and appreciate any constructive criticism to enhance the quality of this and future research endeavours.

I seize the opportunity to thank my supervisor, Patrick Levermore, for his unwavering support throughout this journey. Special thanks also go to Alejandro, Madhav, Pierre, Tobias and Walter for their feedback and enriching conversations.

Chapter 1. AI Alignment

The first chapter of this work serves as an introduction to the complex and multifaceted field of AI alignment (an expert reader may want to skip this chapter).

Overall, the aim is to provide a foundational understanding of the challenges involved in aligning AI systems with human values, setting the stage for subsequent discussions about potential solutions and promising frameworks.

Importantly, when searching for solutions to AI alignment one must consider the scope of the tasks a system is designed to perform. Broad, long-term tasks amplify issues such as specification gaming, deceptive alignment, and scalable oversight. In this work, I argue why bounded, short-term tasks partially mitigate or at least render more tractable...

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