Position: Technical Research and Talent is Needed for Effective AI Governance
Authors: Anka Reuel, Lisa Soder, Benjamin Bucknall, Trond Arne Undheim
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this position paper, we survey policy documents published by public-sector institutions in the EU, US, and China to highlight specific areas of disconnect between the technical requirements necessary for enacting proposed policy actions, and the current technical state of the art. Our analysis motivates a call for tighter integration of the AI/ML research community within AI governance in order to i) catalyse technical research aimed at bridging the gap between current and supposed technical underpinnings of regulatory action, as well as ii) increase the level of technical expertise within governing institutions so as to inform and guide effective governance of AI. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, Stanford University, Stanford, US 2Interface, Brussels, BE 3Centre for the Governance of AI, Oxford, UK 4Oxford Martin AI Governance Initiative, Oxford, UK. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is a position paper and does not describe a new methodology for which open-source code would be provided. |
| Open Datasets | No | The paper does not describe experiments using datasets, thus no training data access is provided. It analyzes policy documents and literature. |
| Dataset Splits | No | The paper does not describe experiments or dataset splits for validation. |
| Hardware Specification | No | The paper does not describe any computational experiments or specify hardware used. |
| Software Dependencies | No | The paper does not describe any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper does not describe any experimental setup details such as hyperparameters or training configurations. |