About Computer

Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed articles written for and by computer researchers and practitioners representing the full spectrum of computing and information technology, from hardware to software and from emerging research to new applications. The aim is to provide more technical substance than trade magazines and more practical ideas than research journals. Computer seeks to deliver useful information for all computing professionals and students, including computer scientists, engineers, and practitioners of all levels.

Special Issue on AI Governance and Compliance
Submission Date: 2026-04-13

A governance system can be understood as the full set of institutional arrangements, including rules and agents who create them, that regulate transactions within and across the boundaries of economic systems (Hollingsworth, Schmitter & Streeck, 1994). These arrangements encompass both state and non-state organizations and operate through formal and informal rules, norms, and beliefs. In the context of artificial intelligence (AI), governance systems determine how data is collected, shared, and safeguarded, how algorithms are trained and deployed, and how accountability is ensured (Shin & Ahmad, 2025). Effective AI governance is therefore critical to balancing innovation with ethical, legal, and social considerations.

New AI-related regulative institutions are rapidly expanding to address these concerns. Some focus narrowly on specific activities, such as employment, while others provide comprehensive frameworks covering the full spectrum of AI use. For instance, in April 2023, New York City introduced definitive guidelines governing the use of automated employment decision tools in hiring and promotion (Paretti, Ray, Freedberg & McPike, 2023). At the same time, broader regulatory initiatives are underway in major jurisdictions including China, the EU, Japan, the U.K., and the U.S., each seeking to establish rules that ensure AI systems are safe, transparent, and accountable.

In many cases, normative frameworks and rules have emerged to fill regulatory gaps, especially where formal agencies remain underdeveloped or absent (Kshetri, 2024). These mechanisms are typically prescriptive rather than coercive, guiding behavior without the force of law. Such institutions include voluntary guidelines and codes of conduct, technical standards, and certification programs, all of which provide structure and accountability in the absence of comprehensive regulation.

As AI technologies rapidly expand across sectors such as healthcare, finance, education, defense, and government, the need to safeguard responsible use, transparency, and accountability has become more pressing than ever. Yet, despite growing recognition of these challenges, governance mechanisms remain at an early stage of development. Regulatory and oversight frameworks often lag behind technological advances, leaving ethical, legal, and operational blind spots that can undermine trust and exacerbate risks.

This special issue of IEEE Computer will address the global conversation around AI governance and compliance. The issue aims to bring together interdisciplinary voices from policy, academia, industry, and civil society to explore strategies for regulating, auditing, and governing AI systems to ensure alignment with human values, social norms, and legal expectations.

AI governance is not only a matter of technical risk management but also of societal trust and democratic accountability (Floridi et al., 2018; Mittelstadt, 2019; Shin et. al, 2024). This issue will spotlight global efforts to develop comprehensive regulatory frameworks such as the EU AI Act (European Commission, 2021), the NIST AI Risk Management Framework (NIST, 2023), and efforts by organizations like OECD, ISO/IEC, and IEEE Standards Association.

We invite high-quality, original contributions that explore topics including, but not limited to the following:

    Comparative regulatory frameworks for AI
        e.g., EU AI Act, U.S. Blueprint for an AI Bill of Rights, China’s AI governance initiatives, UAE’s AI ethics guidelines.
    AI risk management and compliance engineering
        Strategies for operationalizing frameworks like the NIST AI RMF; internal governance audits and lifecycle assurance.
    Responsible AI implementation in practice
        Sectoral case studies demonstrating organizational compliance programs and ethical deployment mechanisms.
    Algorithmic transparency and explainability
        Technical and policy mechanisms for model interpretability in high-risk domains 
    Data governance and privacy regulations
        Navigating compliance with GDPR, CCPA, and other cross-border data protection laws; consent, data sovereignty, and digital identity.
    Bias detection and mitigation
        Evaluation methods and interventions to reduce algorithmic harm, especially in high-stakes applications.
    Human-in-the-loop systems and oversight
        Design implications of shared accountability between humans and machines.
    Ethics committees and AI oversight boards
        Institutional innovations for decision review, stakeholder engagement, and social acceptability of AI tools.
    Compliance automation and AI governance toolkits
        Tools, standards, and infrastructures enabling “compliance by design” approaches.
    Normative frameworks and rules for AI governance
        Voluntary guidelines and codes of conduct, technical standards, and certification programs.
    Low- and middle-income countries and governance capacity
        Inclusion challenges in global regulatory development and digital colonialism risks.
    Future-forward discussions
        Anticipatory regulation and scenario analysis for foundation models, generative AI, and autonomous systems.