Part 4/14:
Transparency and Explainability: Clear understanding of how models arrive at predictions enhances user trust. Explainable AI (XAI) provides insights into the decision process, enabling subject matter experts to reconcile differences and make informed judgments.
Accountability: AI models must be held responsible for their performance. As business environments evolve, models should alert users when their accuracy diminishes, signaling the need for retraining or review.
Compliance: Ensuring AI workflows adhere to regulatory standards protects organizations from legal and reputational risks. Transparency also facilitates compliance by providing auditable decision trails.