AI Transparency is the principle that organizations should be open about how their AI systems work, what data they use, how decisions are made, and what limitations exist. It is a foundational requirement in most AI governance frameworks and regulations.
Transparency has multiple dimensions: Algorithmic Transparency (understanding how the AI model works and makes decisions), Data Transparency (clarity on what data is collected, how it's processed, and who has access), Decision Transparency (explaining individual AI decisions to affected parties), and Organizational Transparency (disclosing AI usage to customers, employees, and regulators).
Regulatory requirements increasingly mandate AI transparency: the EU AI Act requires disclosure when interacting with AI systems, GDPR gives individuals the right to an explanation of automated decisions, and various industry regulations require documentation of AI-assisted processes.
Implementation includes: model documentation and model cards, decision explanation systems (XAI), AI usage disclosures in products and services, regular transparency reports, audit trails for AI decisions, and clear communication about AI capabilities and limitations.
