Responsible Generative AI Checklist
Tags: Frameworks, Regulatory
TL;DR
- Practical checklist spanning ethics, transparency, data privacy, content quality, and governance
- Emphasizes disclosure, bias testing, secure data handling, and human oversight
Why it matters for HK marketers: It reduces compliance and brand risk while scaling AI use in campaigns.
Core pillars
Ethical use and fairness
- Comply with data protection laws (e.g., GDPR, CCPA)
- Prevent discriminatory, harmful, or biased outputs
- Test for unintended bias and cultural sensitivity; use diverse datasets
- Establish internal AI ethics guidelines
Transparency and accountability
- Disclose AI-generated content to consumers and stakeholders
- Educate teams on system limits and best practices
- Share AI’s role with clients/partners and document decisions/outputs
- Notify consumers during AI-driven interactions
Data privacy and security
- Secure data and avoid using sensitive PII in training
- Be cautious with attributes like ethnicity, religion, or minors in profiling
- Use clean rooms/secure environments for data collaboration
- Clarify data/model ownership and support deletion requests
Content quality and brand alignment
- Validate factual accuracy and enforce brand guidelines
- Maintain human oversight and review processes
- Balance automation with human creativity for authenticity
Governance and sustainability
- Regularly audit AI for performance and ethics
- Assign cross-functional governance and update practices with new regulations/tech
- Favor eco-efficient workflows and platforms
So what for marketers
Adopt this checklist as a gating tool for any AI use case; require teams to document adherence before launch.
Sources:- IAB_GenerativeAIPlaybook_January_26 (1).pdf
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