C2: Ethical Dilemmas in AI

Exploring ethical challenges in AI, including bias, privacy, and accountability in AI-driven systems.

Understanding the Challenge

As AI continues to shape our world, ethical considerations have become a cornerstone of responsible innovation. Key challenges include:

Addressing and mitigating bias in AI systems and decision-making processes.

Ensuring the protection of personal data and privacy in AI applications.

Making AI decision-making processes more interpretable and understandable.

Establishing clear guidelines and governance structures for AI development and deployment.

Mitigate Bias: Develop diverse training datasets and implement fairness constraints in AI models.
Enhance Privacy: Use techniques like federated learning and differential privacy to protect individual data.
Improve Transparency: Implement explainable AI (XAI) techniques to make AI decision-making processes more interpretable.
Ensure Accountability: Establish clear guidelines and governance structures for AI development and deployment.

Healthcare: Using privacy-preserving techniques when handling sensitive patient data in AI-driven diagnostics.
Finance: Auditing AI models for bias to ensure fair lending practices.
Criminal Justice: Employing explainable AI to provide transparency in risk assessment tools.

IBM AI Fairness 360: Detect and mitigate bias in machine learning models.
Google's What-If Tool: Explore model behavior and understand feature impacts.
Microsoft's InterpretML: Train interpretable models and explain black-box systems.