What ethical challenges arise when deploying AI Agents, and how do you address them?

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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