COMPEL Glossary / transparency
Transparency
Transparency in AI governance is the principle that organizations should openly communicate about their use of AI, how AI systems make decisions, what data they use, what their limitations are, and what governance mechanisms are in place.
What this means in practice
Transparency operates at multiple levels: system transparency (how does this specific model work?), organizational transparency (what AI does this company use and why?), and outcome transparency (what effects are AI decisions having on people and communities?). For organizations, transparency builds trust with stakeholders but must be balanced against legitimate confidentiality interests such as intellectual property and security. In COMPEL, transparency is a core governance principle assessed during Calibrate and designed into governance frameworks during Model, with specific attention to transparency boundaries in Module 3.4 on governance architecture.
Why it matters
Transparency builds stakeholder trust by openly communicating how AI systems make decisions, what data they use, and what governance mechanisms are in place. However, transparency must be balanced against legitimate confidentiality interests including intellectual property and security. Organizations that achieve the right transparency balance earn the trust that enables broader AI deployment, while opaque organizations face increasing regulatory scrutiny and stakeholder resistance.
How COMPEL uses it
Transparency is a core governance principle assessed during the Calibrate stage and designed into governance frameworks during the Model stage. The Governance pillar (D14-D18) defines transparency requirements with specific attention to transparency boundaries in Module 3.4. The Produce stage implements transparency mechanisms, and the Evaluate stage measures stakeholder perception of organizational transparency about AI usage.
Related Terms
Other glossary terms mentioned in this entry's definition and context.