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.
Requires login.
Related Concepts (2)
Similar Concepts
- accountability and transparency in ai
- accountability in ai systems
- ensuring fairness and transparency in ai algorithms
- explainable ai
- explainable ai in autonomous systems
- explainable ai in healthcare
- fairness and accountability in ai
- robustness and reliability of ai systems
- transparency and accountability in ai governance
- transparency and explainability in ai
- transparency and interpretability in ai control
- transparency and interpretability of ai models
- transparency in ai decision-making processes
- transparency in ai systems
- trust and accountability in ai systems