International Journal of Digital Law and Governance

About this journal

Objective
International Journal of Digital Law and Governance (IJDLG) is a fully peer-reviewed forum for theoretical and applied research in digital law and governance. The journal is international in scope, aiming to cover topics relating to digital law and governance throughout the world. Of particular interest is the interface between law and the following digital topics (but not limited to): cybercrime, cyber bullying, online harassment, online rumor and misinformation, information and data protection, virtual assets, digital governance, hacking, artificial intelligence, and technology and ethics. The journal analyzes the foregoing digital issues from a multidisciplinary perspective, drawing upon insights, concepts and theories from law, management, computer science, communication, economics, political science, and information science. The journal will be of interest to legal scholars, legal practitioners, government officials, data scientists, and digital engineer.

Topics

Artificial Intelligence and Law
Computer Law
Cybersecurity and governance
Cyber affairs and law
Data and Information law
Digital Forensics
Digital and Internet Policy
Information Technology and Law
Internet Research

Article formats
Original Research Articles, Review Articles, Book Reviews

Your Benefits

High quality research in digital law and governance
In-depth discussions led by law professionals, policy makers and digital experts
Interdisciplinary approach to digital law and governance
Internationally recognized reviewers
Open Access for immediate and wide spreading
Rapid online publication of contributions as “ahead-of-print” versions

Open Access
Judging Data: Critical Discourse and the Rise of Data Intellectual Property Rights in Chinese Courts

August 21, 2025

Chanhou Lou

Download PDF

This paper examines how Sino-judicial activism shapes Data Intellectual Property Rights (DIPR) in China through Critical Discourse Analysis (CDA). It identifies two complementary judicial discourses: local courts such as the Zhejiang High People’s Court (HCZJ) adopt a judicial continuation discourse that pragmatically extends intellectual property norms to data disputes, while the Supreme People’s Court (SPC) employs a judicial linkage discourse aligning judicial reasoning with national policy and administrative governance. Their interaction forms a Bidirectional Conceptual Coupling (BCC) – an “inside-out” projection of local court reasoning and an “outside-in” translation of policy into judicial interpretation by the SPC – that mutually legitimizes and constrains judicial and policy frameworks, balancing the demand for unified market standards with safeguards against platform-driven monopolization. Through cases such as the HCZJ’s Taobao v. Meijing and the SPC’s Anti-Unfair Competition Interpretation , the study shows DIPR as an experimental field for doctrinal innovation and institutional coordination in China’s evolving digital governance.
Open Access
Navigating the Ethical Landscape of AI Innovation: Challenges and Opportunities

June 11, 2025

Qiyan Mao, Ming Xu

Download PDF

The rapid development of Artificial Intelligence (AI) is reshaping the global landscape; however, this progress has concurrently given rise to a series of profound ethical and legal challenges. Issues such as algorithmic bias, lack of transparency, and insufficient privacy protection not only threaten technology’s credibility but also pose systemic risks to human rights protection, social stability, and even national security. Striking a dynamic balance between rapid technological innovation and effective risk regulation has emerged as a critical issue in the realm of global AI governance. In order to investigate the synergistic evolution between technological innovation and normative constraints, and to facilitate responsible development and sustainable governance of AI, this study addresses the aforementioned challenges. It integrates legal and ethical perspectives to conduct a comparative analysis alongside case studies of major global AI governance models, and to explore strategies for establishing an adaptive regulatory framework amidst the dynamic interplay of technological advancement and risk evolution. On this basis, the proposed Dynamic Interactive Double-helix Model for AI Governance–the “Technology-driven Innovation Axis” and the “Ethical-legal Constraint Axis”–facilitates the simultaneous enhancement of its security and development through dynamic interaction. It emphasizes the intrinsic driving force of technological progress as well as the embedded guarantee of ethical values, in an attempt to provide an innovative solution for the global AI governance system that combines both theoretical depth and practical feasibility.
Open Access
Integration of Soft and Hard Laws: Profiling Legal Protection for “AI for All”

April 25, 2025

Yang Xiao, Xiaoxia Sun

Download PDF

This paper analyzes the concept of “AI for All” (AIFA), exploring its theoretical foundations, challenges, and pathways for legal implementation. AIFA encompasses three key dimensions: “AI Technology for All”, “AI Justice for All”, and “AI for All Countries”. However, the rise of generative AI – with costly subscription models – threatens these ideals, exacerbating economic and cognitive disparities. Currently, AIFA governance relies on non-binding soft laws, which lack enforceability, while hard laws remain underdeveloped. To prevent AIFA from a utopian vision descending into a dystopian reality, this paper argues for a dual approach: (1) expanding soft laws to explicitly address affordability, literacy, and international cooperation, and (2) tailoring hard laws based on national AI capacity – ranging from pricing regulations in core AI states to global cooperation for peripheral nations. By integrating soft law’s ethical guidance with hard law’s enforcement, AI governance can balance innovation with inclusivity, ensuring AI benefits all rather than deepening inequalities.
Open Access
The Alignment of Values: Embedding Human Dignity in Algorithmic Bias Governance for the AGI Era

April 25, 2025

Yilin Zhao, Zeshen Ren

Download PDF

Decision-makers across both technological and political fields increasingly recognize the need for AI regulation. In the context of AI governance, alignment refers to the requirement that AI systems operate in accordance with human values and interests. This article argues that misalignment is a key driver of algorithmic bias, which not only perpetuates rights infringements but also undermines AI safety, posing risks to its societal integration. This alignment imperative is rooted in the enduring principle of human dignity, a juridical concept that has evolved from its origins in Roman jurisprudence to its establishment as a cornerstone of modern constitutional democracies. Today, human dignity serves as a foundational value underpinning the rule of law. Through comparative legal analysis, this article examines how human dignity informs algorithmic governance across major jurisdictions, analyzing regulatory texts, directives, and case law addressing AI-related challenges. Despite varying implementation approaches, this paper demonstrates that human dignity can serve as a universal foundation for AI governance across cultural contexts. While the European Union prioritizes human dignity in regulating algorithmic bias, emphasizing individual rights, public interests, and human oversight, this principle extends beyond European law, offering a normative anchor for global AI governance. The article concludes with governance recommendations for the AGI era, advocating for the integration of human dignity into AI alignment. This requires both embedding dignity-preserving constraints at the technical level and developing robust assessment frameworks capable of evaluating increasingly advanced AI systems. As AI surpasses human intelligence, governance mechanisms must ensure these systems align with ethical principles, remain under meaningful human control, and operate within legally and socially acceptable boundaries.
Open Access
Privacy Protection on Social Media Platforms: Overdisclosure of Online Behavioral Data is Labeling Users

April 25, 2025

Le Cheng, Yijin Guo

Download PDF

In the digital age, the protection of privacy and personal data on social media platforms has become increasingly significant. Online behavioral data includes user interactions, fandom affiliations, and engagement records, simultaneously encompassing both defensive privacy expectations and reasonable use expectations – blurring the lines between different levels of personal data protection. The sensitivity of online behavioral data, when aggregated, generates a “digital identity” that reflects a natural person’s social identity and is closely linked to individual personality, resulting in high identifiability and privacy risks. Due to the highly commercialized use of personal data by social media platforms and the ineffectiveness of the informed consent mechanism, legal frameworks primarily focus on traditional sensitive personal data, leaving the handling of online behavioral data inadequately regulated despite its potential to cause privacy risks. By analyzing consent mechanisms and case studies from platforms such as Weibo, this research highlights how default platform settings prioritize disclosure over user control and why current informed consent mechanisms are inadequate. This study advocates for a more comprehensive boundary delineation of sensitive personal data, a renewed stratification of personal data in the digital age, and stricter informed consent mechanisms to strengthen privacy protections, aligning platform regulations with contextual privacy expectations.
Open Access
Big Data Versus Big GPU: Evolving Requirements and Governance Dynamics of AI Training Data

April 18, 2025

Le Cheng, Xuan Gong, Yun Zhao

Download PDF

Pre-trained large language models (LLMs), epitomized by ChatGPT, have leveraged a cornucopia of “big data” to attain substantial leaps in artificial intelligence (AI). Whereas the diminishing returns from pre-training and the depletion of available training data have become evident, the post-training scaling law bolstered by “big GPU” has surfaced as an overriding strategy. Since 2024, post-trained models exemplified by o1 and DeepSeek-R1 have been widely acclaimed as successes in logic-intensive fields like advanced scientific problem-solving, serving as a bellwether for artificial general intelligence (AGI). Driven by the two cardinal elements of computing power and task-specific datasets, the data training processes of post-trained models exhibit more erratic and uncontrollable tendencies, which may be a menace to core societal domains and precipitate systemic friction vis-à-vis the existing data governance derived from pre-trained models. At this watershed moment, this article aims to conduct a comprehensive comparison of training data paradigms between pre-trained and post-trained models and to further develop cogent and favorable governance responses to mitigate emerging risks. Consequently, data security must be established as a prerequisite for AI development, and a lifecycle-based governance framework for AI training data in blended models can be introduced in the metamorphosis toward “bigger AI models”.
Open Access
Profiling Elements, Risks, and Governance of Artificial Intelligence: Implications from DeepSeek

April 18, 2025

Le Cheng, Ming Hu, Tao Hong

Download PDF
Open Access
Opportunities, Challenges, and Regulatory Responses to China’s AI Computing Power Development under DeepSeek’s Changing Landscape

April 18, 2025

Tao Hong, Ming Hu

Download PDF

DeepSeek marks a significant advancement in artificial intelligence (AI), ushering in a new phase of development that is reshaping the global AI landscape. AI progress has relied on the assumption that increasing computing power drives better outcomes. Yet, this focus has yielded diminishing returns, amplifying problems like market monopolies and unaddressed risks. By making its model freely available through open-sourcing, DeepSeek revitalizes the market and uses techniques like model distillation to ensure broader access to computing resources. For China, DeepSeek offers a strategic chance to close the gap with global AI leaders. However, efficiency gains in DeepSeek’s design unexpectedly increase computing demand – a phenomenon known as the Jevons paradox. Meanwhile, China grapples with limited computing resources, heightened security concerns, and stricter U.S. chip export restrictions. These difficulties underscore a deeper tension between current infrastructure and fast-evolving technology. Given computing power’s dual role, alongside China’s industrial needs and global competitive dynamics, regulation should prioritize enabling growth. This facilitative regulatory framework demands dynamic demarcation between public and private spheres in computing infrastructure governance. It also calls for advancing standardization, securing the entire supply chain, and adopting adaptable governance models. Globally, computing power governance is shifting toward “competitive cooperation,” where nations collaborate amid rivalry. China can lead by pushing for international standards, contributing its expertise to a secure, stable, and mutually beneficial global computing ecosystem.
Open Access
Charting the Landscape of Artificial Intelligence Ethics: A Bibliometric Analysis

April 18, 2025

Jiaxuan Qiu, Le Cheng, Jin Huang

Download PDF

Using bibliometric methods, this study systematically analyzes 6,084 AI ethics-related articles from the Web of Science Core Collection (2015–2025), capturing both recent advances and near-future directions in the field. It begins by examining publication trends, disciplinary categories, leading journals, and major contributing institutions/countries. Subsequently, co-citation (journals, authors, references) and keyword clustering methods reveal the foundational knowledge structure and highlight emerging research hotspots. The findings indicate increasing interdisciplinary convergence and international collaboration in AI ethics, with core themes focusing on algorithmic fairness, privacy and data security, ethical governance in autonomous vehicles, medical AI applications, educational technology, and challenges posed by generative AI (e.g., large language models). Burst keyword detection further shows an evolutionary shift from theoretical debates toward practical implementation strategies and regulatory framework development. Although numerous global initiatives have been introduced to guide AI ethics, broad consensus remains elusive, underscoring the need for enhanced cross-disciplinary and international cooperation. This research provides valuable insights for scholars, policymakers, and industry practitioners, laying a foundation for sustainable and responsible AI development.
Open Access
Towards Just AI: Challenges and Solution Framework for Algorithmic Discrimination in Judicial System

March 5, 2025

Landuo Dou, Xiaodong Dou

Download PDF

With the rapid development of technologies like big data, artificial intelligence (herein after AI), and blockchain, society is ushering into a new era of digital civilization. However, the same algorithms that assist in efficient decision-making for human society may also introduce issues of algorithmic discrimination. Therefore, this paper focuses on the judicial domain to deeply explore potential instances of algorithmic discrimination in AI. It identifies three key dimensions of algorithmic discrimination risks in the judicial AI domain: the ambiguity of algorithm usage boundaries, the diversity in discriminatory outcomes, and the injustice within the algorithmic environment. Building upon this, the author examines global AI governance landscapes to extract corresponding governance strategies and practical insights. Finally, a systematic regulatory approach for addressing algorithmic discrimination in judicial AI is proposed, unfolding across three levels and nine aspects: making algorithmic limitations explicit, diversifying algorithmic regulations, and justifying the algorithmic environment. This framework aims to contribute to a more reasonable, systematic, and just governance of algorithms in judicial AI.

Issue 0

Issue 2

Issue 1

Submission

You can easily submit your manuscript online. Simply go to http://mc.manuscriptcentral.com/ijdlg and you will be guided through the whole submission and publishing process.

Before preparing a manuscript, please read the journal’s Instructions for Authors and have a look at our Ethical Guidelines and our License to publish.
Please note that your manuscript may be checked for plagiarism via Crossref Similarity Check.

Your benefits of publishing with us

Open Access publication
Research freely available for all to read and download
Authors retain copyright – articles published under the Creative Commons Attribution (CC-BY-4.0) License
Rapid publication times
Free publication of color figures
Every article easily discoverable because of Search Engine Optimization (SEO) and comprehensive abstracting and indexing services
Professional sales and marketing activities

Submission process

Submission of your manuscript via our submission management tool following the Instructions for Authors
Single-blind peer review process
A decision on your paper is aspired within 4-5 weeks
Accepted articles are published online approx. 3 weeks after acceptance

Please note

Publication language is English. Manuscripts must be written in clear and concise English
The Template for Ethical and Legal Declarations is required for submission (needs to be customized by the submitting author on behalf of all others and uploaded as a separate doc-file at the time of the original submission)
If you have any general questions please visit our FAQ for authors
For more detailed information please contact the IJDLG Editorial Office chengle [at] zju.edu.cn

We are looking forward to receiving your manuscript!

Editors-in-Chief
Le Cheng (Zhejiang University, China)
Ming Hu (Zhejiang University, China)

Assistant Editor
Yilin Zhao (Zhejiang University, China)

Book Reviews Editor
Wei Yu (The University of Melbourne, Australia)

Editorial Board
Christoph Busch (University of Osnabrück, Germany)
Julien Chaisse (City University of Hong Kong, China)
Laura Ervo (Örebro University, Sweden)
Alberto De Franceschi (University of Ferrara, Italy)
Enrico Francesconi (Italian National Research Council, Italy)
Craig Hoffman (Georgetown University, USA)
Sarah Marusek (University of Hawai‘i, USA)
Aleksandra Matulewska (Adam Mickiewicz University, Poland)
Karen McAuliffe (University of Birmingham, UK)
Rostam J. Neuwirth (University of Macau, China)
Jianzhong Shi (China University of Political Science and Law, China)
King-kui Sin (University of Wollongong, Australia)
Paul Torremans (University of Nottingham, UK)
Anne Wagner (University of Lille, France)
Guiguo Wang (Zhejiang University, China)
Minghui Xiong (Zhejiang University, China)
Yun Zhao (The University of Hong Kong, Hong Kong, China)

International Journal of Digital Law and Governance is covered by the following services:

CNPIEC - cnpLINKer
Dimensions
EBSCO Discovery Service
Google Scholar
J-Gate
Naver Academic
Naviga (Softweco)
Primo Central (ExLibris)
ReadCube
ScienceON (Korea Institute of Science and Technology Information)
Scilit
Semantic Scholar
Summon (ProQuest)
TDNet
WorldCat (OCLC)
X-MOL
Yewno Discover

Law Law, other

Online Submission of Manuscripts

Journal information

Additional information

eISSN:

2941-2552

Language:

English

Publisher:

De Gruyter

Additional information

First published:

March 30, 2024

Publication Frequency:

2 issues per year

International Journal of Digital Law and Governance

Overview

About this journal

Your Benefits

Ahead of Publication / Just Accepted

Issues

Submit

Editorial

Abstracting and Indexing

Supplementary Materials

Supplementary Materials

Topics

Topics

External Links

Bibliographic data