Call for papers: Governance, Safety, and Ethics in AI for Marketing Analytics

The Challenge

The AI pilot worked. Marketing analytics teams have built impressive prototypes: customer segmentation models, predictive lead scoring, content personalisation engines, and sentiment analysis tools. But as these initiatives move toward production, a different set of problems emerges. Questions of governance, safety, and ethics are no longer theoretical; they are operational.

Who decides what data the AI can use? Who is accountable when a personalisation algorithm reinforces bias? What happens when AI-driven segmentation inadvertently discriminates against certain groups? How do organisations ensure that AI-generated marketing insights are transparent, explainable, and aligned with both regulatory requirements and customer expectations? These are not theoretical questions; they are the central challenges facing every organisation scaling AI in marketing analytics today.

Research from McKinsey, Gartner, and Forrester consistently points to the same finding: AI governance failures are the primary barrier to developing AI models beyond pilot projects. In marketing, the stakes are compounded by direct customer impact, regulatory scrutiny (the EU AI Act, GDPR, CCPA, and emerging US state-level legislation), and reputational risk that can erode brand trust overnight.

AI Governance Frameworks for Marketing

• Enterprise AI governance structures applied to marketing analytics: policies, controls, decision rights, and oversight mechanisms
• Governance maturity models for marketing AI: from ad hoc to optimised
• The role of AI Centres of Excellence in marketing organisations
• Cross-functional alignment challenges: aligning marketing, IT, legal, compliance, and data teams around AI governance
• Governance for AI-driven customer data platforms and behavioural analytics

Safety and Risk in AI Marketing Analytics

• Risk frameworks for AI-driven personalisation, targeting, and content generation
• Safety controls for generative AI in marketing – preventing hallucinations, misinformation, and brand-damaging mistakes
• Human-in-the-loop safeguards for high-stakes marketing decisions
• Incident response: what happens when AI marketing systems produce harmful or inaccurate outputs?
• Content governance and knowledge readiness as prerequisites for safe AI performance in marketing

Ethics, Fairness, and Transparency

• Bias detection and mitigation in AI-driven segmentation, targeting, and media allocation
• Fairness in algorithmic decision-making for marketing: measuring and addressing disparate impact
• Transparency and explainability in AI-driven marketing analytics: what customers and regulators require
• The ethics of AI-driven persuasion, behavioural nudging, and dark patterns in marketing
• Responsible use of synthetic data and AI-generated content in customer-facing communications

Regulatory Compliance and Data Privacy

• The EU AI Act, GDPR, CCPA, and emerging regulations: implications for AI-driven marketing analytics
• Privacy-preserving analytics: federated learning, differential privacy, and clean rooms in marketing AI
• Consent management and data governance in the context of AI personalisation
• Navigating multi-jurisdictional compliance in global marketing AI deployments
• Regulatory impact on marketing attribution, media mix modelling, and programmatic advertising

Knowledge Foundations and Information Architecture

• The role of information architecture in enabling governed, safe AI for marketing analytics
• Content readiness and knowledge engineering as prerequisites for responsible AI in marketing
• Taxonomy, metadata, and ontology as governance enablers for AI-driven customer insights
• AI readiness assessments: measuring organisational maturity for governed marketing AI
• Data quality, content structure, and semantic clarity as foundations for trustworthy AI marketing outputs

Organisational Readiness and Change Management

• Building a culture of responsible AI in marketing organisations
• Executive alignment and leadership support for AI governance in marketing
• Training marketing teams for safe and ethical AI use
• The business case for AI governance: ROI, risk reduction, and competitive advantage
• Case studies of successful (and unsuccessful) AI governance implementations in marketing

• Practice Articles: Thought pieces, best practice articles, case studies, new approaches, technologies and techniques, market and consumer research, legal and regulatory updates, and other contributions written by practitioners. All case studies must address the following questions: What has worked? Why has it worked? What lessons were learned? How could it be done elsewhere? Practice articles should be 3,000 to 5,000 words in length.
• Research Papers: Contributions that explore new models, theories, and applied research in AI governance, safety, and ethics for marketing analytics. Research papers must have clear implications for professionals and business practice. Research papers should be around 6,000 words in length.

• Written in the third person with a formal, professional tone. Personal pronouns and slang should be avoided.
• Manuscripts must not be promotional or marketing vehicles.
• References should follow the Vancouver system (superscript numbering in text and full citation list).
• Figures, tables, and graphs must be submitted in original Word/Excel formats with clear captions and sources, and be understandable in black and white.
• Papers must include an abstract (up to 300 words) summarising the purpose, methodology, findings, and implications, plus 4–7 keywords.
• A brief author biography (up to 200 words) is required.
• Acronyms must be spelled out in full on first use.
• Metric units should be used throughout.
• Photographs and illustrations should be submitted in high resolution  as per guidelines.