Jump to:

• Introduction
• Policy Significance
• Key Themes
• Timetable
• How to Submit
• Editors

Introduction

Following a successful track at the Data for Policy Conference 2024, we are issuing an open call for contributions to a special collection (virtual special issue) on "Generative AI for Sound Decision-Making: Challenges and Opportunities” to published in Data & Policy, an open-access journal at Cambridge University Press.

This collection will investigate the impacts of LLMs and other Generative AI Models (GAIs) on our daily life and how regulators can respond to ensure that GAIs make sound recommendations/decisions, for the benefits of humanity and societies.

ChatGPT, the popular chatbot of OpenAI, has been accessed by more than 100 million users monthly the first two months after launching, making itself the fastest-growing consumer application. Its phenomenal success attracts substantial business and public interests. LLMs continues to break boundaries and be vastly applied in different fields, e.g. education, healthcare, law/public policy, etc., tremendously benefitting citizens of different communities and facilitates public decision-makings. However, results/decisions generated by LLMs/GAIs have demonstrated to be equally confusing and biased when the training datasets are distorted or unevenly distributed across different sub-populations. A deeper understanding of the potentials and limitations of LLMs/GAIs is critical for the future advancement of LLMs/GAIs, and the future sustainability of humanity and societies.

This special collection of papers aims to explore the technical and associated moral and ethical challenges posed by LLMs/GAIs, and what moral and ethical guidelines should be in place for addressing these challenges. We will explore the trustworthiness and explainability of existing LLMs/GAIs, and when LLMs/GAIs can be considered trustworthy and explainable. Are there any specific moral and ethical guidelines available to guide the future development of LLMs/GAIs?

Policy significance of this collection

Given that the application of LLMs/GAIs can transform the traditional practices across many fields, it is important for decision makers to properly anticipate the upcoming changes and provide regulatory frameworks to guide the research and development of GAIs for sound decision-makings.

Key themes

The key themes below cover the technical and the associated ethical and moral challenges of LLMs/GAIs, and the opportunities that these models presented for socially beneficial, moral and ethical decision-makings, covering, but not limited to, the following topics:

• Challenges of LLMs/GAIs in decision-makings, such as education, health care, regulatory decision-makings
• Regulatory frameworks and guiding principles of LLMs/GAIs for sound decision-makings
• Methods for determining if LLMs/GAIs are making socially beneficial, moral and ethical decisions
• Methods for evaluating trustworthy and explainable LLMs/GAIs
• Methods to reduce data/model biases and improve fairness
• Methods to reduce disinformation/misinformation and improve trustworthiness
• Methods to improve data privacy and security
• Case studies covering socially beneficial, ethically and morally sound LLMs/GAIs development

Timetable

The initial submission deadline of January 10, 2025 has passed. However, authors who are interesting in submitting to any of the editions of the Data for Policy Conference in 2025 will still have the option to select to be part of this special collection. The submission deadline for Data for Policy 2025 (Europe), which is being held in The Hague, Netherlands on 12-13 June 2025, is February 21, 2025. Submissions to the Conference are made through the journal's system and authors should select the Data for Policy Conference Proceedings Paper article type and the Generative AI for Sound Decision Making tag when prompted to select the relevant special collection. Note that if accepted, one of the authors is expected to attend the conference to present. For more information about the conference, see this page.

Articles will be published as soon as possible after acceptance, in the interest of allowing authors to disseminate their work without unnecessary delay and added to a curated page for the collection of articles. An editorial reflecting on their insights will be published later in the year.

Submission process

Authors should submit articles through the Data & Policy ScholarOne site. Use the Generative AI for Sound Decision-makings dropdown response to the Special Collection question.

Authors submitting to the Data for Policy Conference should use the Data for Policy Proceedings Paper article type.

Editors

• Jacqueline CK Lam (The University of Hong Kong) - Data & Policy Area Editor (Area 5: Algorithmic Governance)
• Victor OK Li (The University of Hong Kong)
• Lawrence Cheung (Chinese University of Hong Kong)
• Jon Crowcroft (University of Cambridge & Alan Turing Institute) - Data & Policy Editor-in-Chief