It is crucial for an organization to have a full understanding of the AI decision-making processes with model monitoring and accountability of AI and not to trust them blindly. Explainable AI can help humans understand and explain machine learning (ML) algorithms, deep learning and neural networks.
ML models are often thought of as black boxes that are impossible to interpret.² Neural networks used in deep learning are some of the hardest for a human to understand. Bias, often based on race, gender, age or location, has been a long-standing risk in training AI models. Further, AI model performance can drift or degrade because production data differs from training data. This makes it crucial for a business to continuously monitor and manage models to promote AI explainability while measuring the business impact of using such algorithms. Explainable AI also helps promote end user trust, model auditability and productive use of AI. It also mitigates compliance, legal, security and reputational risks of production AI.
Explainable AI is one of the key requirements for implementing responsible AI, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability.³ To help adopt AI responsibly, organizations need to embed ethical principles into AI applications and processes by building AI systems based on trust and transparency.