AI Explainability
AI Explainability is the ability to make artificial intelligence system decisions understandable, transparent, and interpretable for stakeholders, regulators, and end users.
What is AI Explainability?
AI Explainability refers to methods and practices that clarify how artificial intelligence models generate outcomes. It ensures that AI decisions can be understood by regulators, businesses, and individuals. Explainability is essential for addressing concerns around fairness, accountability, and bias in AI. Organizations integrate explainability into AI Governance programs to demonstrate compliance, build user trust, and support transparency.
Why AI Explainability Matters
For businesses, AI Explainability builds confidence in AI-driven outcomes by showing how decisions are reached. This transparency improves stakeholder trust, supports ethical use, and reduces reputational and financial risks.
Regulators emphasize explainability in frameworks like the EU AI Act and the GDPR, which require organizations to provide transparency, ensure fairness, and respect individuals’ rights in automated decision-making.
Without explainability, organizations risk regulatory enforcement, user mistrust, and the inability to defend AI-driven outcomes, especially in sensitive contexts such as hiring, lending, or healthcare.
How AI Explainability is Used in Practice
- Providing clear reasoning for AI-driven hiring decisions to ensure fairness and reduce bias.
- Explaining credit risk models in finance to satisfy regulatory requirements and improve customer confidence.
- Documenting AI model behavior for compliance audits under GDPR and the EU AI Act.
- Configuring region-specific explainability reporting to align with local legal obligations.
- Evaluating third-party AI vendors to confirm their systems include explainability features and safeguards.
Related Laws & Standards
- EU General Data Protection Regulation (GDPR)
- EU AI Act
- UK GDPR
- NIS 2 Directive
- Digital Operational Resilience Act (DORA)
How OneTrust Helps with AI Explainability
- Building explainable AI requires documentation, transparency, and accountability. OneTrust helps operationalize AI Explainability by allowing you to:
- Capture and document decision logic in configurable workflows
- Generate transparency reports for GDPR and EU AI Act compliance
- Centralize evidence for regulators and audits
- Collaborate across legal, privacy, and data science teams
- Strengthen accountability with oversight features
- With OneTrust, organizations can deliver AI that is compliant, transparent, and trusted by both users and regulators.
FAQs about AI Explainability
AI Explainability focuses on making decisions understandable through methods like model interpretation, while AI Transparency emphasizes openness about system design, data use, and governance.
AI Explainability focuses on making decisions understandable through methods like model interpretation, while AI Transparency emphasizes openness about system design, data use, and governance.
Explainability helps meet GDPR requirements for transparency and the right to meaningful information, ensuring individuals understand how automated decisions are made.
