Data Quality: ML algorithms rely on high-quality data. Inaccurate or biased data can lead to poor model performance and unintended consequences.
Interpretability: Many ML models, especially complex ones like deep neural networks, are often seen as “black boxes.” Understanding how these models make decisions is crucial, especially in sensitive areas like healthcare and finance.
Bias and Fairness: Algorithms can perpetuate biases present in the training data, leading to unfair outcomes. Ensuring fairness and accountability in ML systems is a significant ethical concern.

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