Explainable AI (XAI) Methods: Interpreting AI Models

What are common challenges when implementing XAI in industry?

Key challenges include balancing accuracy with interpretability, avoiding misleading explanations, and ensuring explanations remain faithful as models evolve. There are also concerns about privacy, bias, and data leakage in explanations, as well as the need for skilled personnel who can design, validate, and communicate explanations effectively. Integrating explainability into existing pipelines, governance processes, and regulatory compliance efforts is another critical hurdle.

How do regulations and ethics influence XAI?

Regulations increasingly require transparency and accountability in automated decision-making. Ethics guide how explanations are presented, ensuring they are non-discriminatory, privacy-preserving, and accessible to affected individuals. Compliance-driven XAI programs emphasize auditable documentation, reproducible evaluation, and human oversight to align AI systems with legal and societal expectations.