Responsible AI in marketing connects model performance with fairness, accountability, transparency, privacy, safety, and broader societal values. It treats trustworthy deployment as a governance concern, not merely a technical one. section 2.4
A practical responsible-AI framework emphasizes:
- Use diverse training data and test models for bias to reduce discriminatory outcomes. section 2.4
- Prefer transparent and explainable AI when marketers must justify decisions or understand outputs. section 2.4
- Establish ethical frameworks, governance structures, and privacy or data protection impact assessments before adopting new technologies. section 2.4
Marketing model review
A model trained on biased data may produce discriminatory outcomes, while an opaque model may prevent marketers from explaining a decision. Bias testing and explainability expose these risks before deployment. section 2.4
