What are the main challenges in AI inspection?

What are the main challenges in AI inspection?

Some key challenges include data quality, bias in algorithms, and the interpretability of AI decisions.

How does data quality affect AI inspection?

Data quality is crucial because poor data can lead to inaccurate inspections, resulting in faulty conclusions.

Can you explain bias in algorithms?

Bias in algorithms occurs when the AI system reflects prejudices present in the training data, leading to unfair outcomes.

What can be done to improve interpretability?

Improving interpretability can involve using simpler models, providing visual explanations, and enhancing user training.

Are there any tools to help with AI inspection?

Yes, there are several tools like LIME, SHAP, and IBM Watson that assist in making AI models more interpretable.