What is Explainable AI?
Explainable AI, often abbreviated as XAI, refers to a set of processes and methods that make the outcomes and operations of artificial intelligence systems understandable to humans. The main purpose of Explainable AI is to provide clear, human-friendly explanations for how AI models make decisions, predictions, or classifications. This is especially important as AI technologies are increasingly used in areas like healthcare, finance, and legal systems, where stakeholders need to trust, verify, and sometimes challenge the decisions made by algorithms. By making AI models more transparent, XAI helps developers, users, and regulators understand why a system behaved in a certain way, which builds trust, supports accountability, and enables better decision-making.
Black box models are AI or machine learning systems whose internal logic and decision-making processes are not easily understood by humans. These models can produce highly accurate results but are problematic because they offer little insight into how or why they reached a specific outcome. This lack of transparency can lead to issues with trust, accountability, and the ability to detect or correct errors and biases.
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Explainable AI, often abbreviated as XAI, refers to a set of processes and methods that make the outcomes and operations of artificial intelligence systems understandable to humans. The main purpose of Explainable AI is to provide clear, human-friendly explanations for how AI models make decisions, predictions, or classifications. This is especially important as AI technologies are increasingly used in areas like healthcare, finance, and legal systems, where stakeholders need to trust, verify, and sometimes challenge the decisions made by algorithms. By making AI models more transparent, XAI helps developers, users, and regulators understand why a system behaved in a certain way, which builds trust, supports accountability, and enables better decision-making.
Black box models are AI or machine learning systems whose internal logic and decision-making processes are not easily understood by humans. These models can produce highly accurate results but are problematic because they offer little insight into how or why they reached a specific outcome. This lack of transparency can lead to issues with trust, accountability, and the ability to detect or correct errors and biases.
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