transparency and explainability in ai systems

Transparency refers to the ability to clearly understand and examine the inner workings of AI systems, including the data, algorithms, and decision-making processes involved. It involves making AI systems more open and accessible, enabling users and stakeholders to comprehend how these systems operate and how they arrive at their conclusions. Explainability, on the other hand, focuses on providing understandable and intelligible explanations for the actions and outputs produced by AI systems. It aims to bridge the gap between the black-box nature of complex AI algorithms and the need for users to comprehend the reasoning behind the decisions made by these systems. In sum, transparency and explainability in AI systems involve increasing openness and accessibility while providing clear explanations to enhance user understanding and trust in these systems.

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