What are the future challenges of machine learning?

What are the future challenges of machine learning?

Some future challenges include data privacy, algorithmic bias, and the need for explainability in AI systems.

How can we address algorithmic bias in AI?

Addressing algorithmic bias requires diverse training data, regular audits, and implementing fairness metrics in models.

What role does data privacy play in machine learning?

Data privacy is crucial as it ensures user trust and compliance with regulations like GDPR, impacting data collection methods.

Can you give examples of explainability in AI?

Examples include using LIME or SHAP to interpret model predictions, making AI decisions more transparent to users.

What skills will be important for future ML practitioners?

Future ML practitioners will need skills in ethics, data governance, and interdisciplinary collaboration to tackle emerging challenges.