Transparency
The disclosure of how AI systems work, what data they use, and how decisions are made.
Transparency in AI means disclosing how it works, what data it uses, and decision logic. EU AI Act makes it mandatory. Model Cards are the standard.
Explanation
Transparency levels: Technical (architecture, training), operative (decision logic), output (is content AI-generated?). EU AI Act requires transparency. Model Cards document model details.
Marketing Relevance
Marketing must be transparent: AI-generated content must be labeled. Personalization logic must be explainable. Trust through openness.
Example
Instagram automatically labels AI-generated images. A chatbot discloses: "I am an AI assistant" before users share details.
Common Pitfalls
Too much transparency can overwhelm. Technical details incomprehensible to laypeople. Trade secrets vs. openness.
Origin & History
Google introduced Model Cards in 2019. EU AI Act (2024) and DSA (2022) mandate algorithmic transparency. Social media platforms must explain recommendation systems.
Comparisons & Differences
Transparency vs. Explainability
Transparency reveals the "what" (system details); Explainability explains the "why" (individual decisions).
Transparency vs. Interpretability
Interpretability means inherent understandability; Transparency means active disclosure – even black boxes can be transparently documented.