AI Marketing Ethics Guidelines

CORE PRINCIPLES

The Human Standard for AI

AI assists human work.
AI never replaces human judgment.
AI outputs require human review.
Humans are accountable for AI outputs.

Ethics Framework

Principle	Definition	Application
Transparency	Be honest about AI use	Disclose when required
Authenticity	Content reflects real expertise	Human review all outputs
Accuracy	Information is verified	Fact-check AI outputs
Privacy	Protect data	Follow data policies
Fairness	Avoid bias	Review for bias
Accountability	Own the output	Human signs off

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TRANSPARENCY REQUIREMENTS

Disclosure Matrix

Content Type	Disclosure Required	How
AI-generated image	Yes	Label or caption
AI voice/avatar	Yes	Clear statement
AI-assisted copy	Typically no	Internal only
AI research	No	Internal only
AI chatbots	Yes	Bot identification
AI personalization	Per regulation	Privacy policy

Disclosure Language

For AI images:

"Image created with AI assistance"

For chatbots:

"You're chatting with our AI assistant. A human team
member is available if needed."

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DATA ETHICS

Data Input Rules

Data Type	AI Use	Restrictions
Public data	Generally OK	Verify accuracy
Customer data	With consent	Privacy compliance
Competitor data	Carefully	Legal review
Employee data	Rarely	HR approval
Confidential data	Never	Prohibited

Data Protection Checklist

• Data is necessary for the task
• Consent exists (if required)
• Data stays within approved tools
• No sensitive data shared with AI
• Output doesn't expose private info

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AUTHENTICITY STANDARDS

Content Authenticity Rules

Requirement	Why	How
Human review	Quality assurance	All content reviewed
Expert input	E-E-A-T	Subject matter review
Original perspective	Differentiation	Add unique insights
Voice consistency	Brand	Human edits for voice
Fact verification	Trust	Check all claims

Red Lines - Never Use AI For

Prohibited	Reason
Fake testimonials	Fraud
Fabricated data	Misinformation
Impersonation	Deception
Plagiarism	IP violation
Fake reviews	Fraud

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BIAS PREVENTION

Bias Check Process

1. Review AI outputs for stereotypes
2. Check for cultural insensitivity
3. Verify representation in images
4. Test with diverse reviewers
5. Document and address issues

Bias Red Flags

Signal	Action
Stereotypical imagery	Regenerate/reject
Gendered language	Edit
Cultural assumptions	Review with DEI
Exclusionary content	Revise

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ACCOUNTABILITY

Responsibility Matrix

Role	Accountability
Creator	Quality of prompt, initial review
Editor	Accuracy, brand, quality
Manager	Process compliance
Legal	Regulatory compliance
Executive	Policy and culture

Incident Response

1. Identify the issue
2. Remove/correct content
3. Document incident
4. Analyze root cause
5. Update processes
6. Train team

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