Part 9/14:
Source attribution: Generative models should ideally "explain" how they generate responses, citing relevant data sources, akin to transparency in classical models.
Confidence and trust metrics: Providing confidence scores continues to be vital, particularly to prevent misinformation or misleading outputs.
Monitoring and remediation: Continuous evaluation of AI outputs enables organizations to detect drift, bias, or inaccuracies and take corrective action promptly.
The core truth persists: trust, accountability, and transparency are the bedrock for sustainable, responsible AI integration—regardless of technological complexity.