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Explainable AI (XAI): Making AI Models Transparent and Trustworthy
- Explainable AI is a process through which the functioning of AI system and the decisions it takes can be easily explained by the AI to the intended users.
- Explainable models provide end-users a clear and understandable reasoning process used by the AI model in arriving at certain results or making decisions.
- Explainability helps increase trust, allows verification, and makes AI systems accountable.
- Explainability is important in critical areas because it builds trust, creates accountability, and transparency by providing access to decision-making processes.
- People are reluctant to trust AI in critical areas like healthcare, finance, and law enforcement if they cannot explain how an AI arrives at a decision.
- Through explainability, the problem of bias patterns is easier to notice, and addressing issues of fairness is made easier.
- The next advances in AI development can be expected to pay more attention to the development of powerful but at the same time explainable AI.
- Explainable AI is crucial in ensuring integrity, credibility and instilling confidence in different industries.
- Companies adopting Explainable AI will bridge the middle ground between artificial intelligence and society.
- It is important to make AI systems' decision-making transparent and easy to comprehend because they are increasingly being integrated in decision-making processes that shape human's life.
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