Answer:
Challenges:
- Bias: Training data may perpetuate inequalities (e.g., hiring algorithms favoring certain demographics) 59.
- Transparency: Black-box decision-making in high-stakes fields like healthcare 7.
- Accountability: Determining responsibility for errors (e.g., autonomous vehicle accidents) 1.
Solutions:
- Bias Mitigation: Use SMOTE for balanced datasets and fairness-aware algorithms 9.
- Explainability Tools: Implement SHAP or LIME to clarify decision logic 9.
- Regulatory Compliance: Adhere to GDPR and ISO standards for data privacy 5.
Example: In insurance, AI Agents audit claims for bias using federated learning to protect sensitive data
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