Explainable AI
What is Explainable AI?
Explainable AI is an upcoming concept in machine learning that addresses how black box decisions of AI systems are made. It investigates and tries to understand the steps and models involved in decisions. It allows one to comprehend the results and trust the output created by machine learning algorithms.
What is the importance of Explainable AI?
ML models are always considered as black boxes that are impossible to interpret. So, it is crucial for an organization to have a full understanding of the AI model decision-making processes and not to trust them blindly.
It describes an AI model, its expected impact, and potential biases. It helps characterize model accuracy, fairness, transparency, and outcomes in AI-powered decision-making. It enables organizations to build trust and confidence when putting AI models into production. It also enables an organization to adopt a responsible approach to AI development.
What are the benefits of Explainable AI?
- Ensure interpretability and explainability of AI models.
- Increase transparency and traceability by simplifying the process of model evaluation.
- Reduce costly errors and the risk of unintended biases.
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