Advanced ML Topics & Ethical Considerations (Weeks 14)
The module explores advanced topics in machine learning, focusing on the ethical and societal implications related to AI systems. It emphasizes the importance of bias detection and mitigation, accountability, transparency, and privacy within AI development. The introduction of explainable AI (XAI) methods like LIME and SHAP underpins the need for interpretability in complex models to ensure they are ethical and trustworthy in real-world applications.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Bias can enter machine learning systems through various stages, leading to unfair outcomes.
- Accountability, transparency, and privacy are critical ethical pillars in the deployment of AI technologies.
- Explainable AI techniques like LIME and SHAP are essential for making model decisions interpretable and understanding their implications.
Key Concepts
- -- Bias and Fairness
- Refers to systematic prejudices embedded within AI systems causing inequitable outcomes, emphasized in the design and deployment of ML models.
- -- Accountability in AI
- The ability to assign responsibility for the outcomes produced by AI systems, essential for public trust and ethical compliance.
- -- Explainable AI (XAI)
- A field focused on making AI model decisions comprehensible to humans, enabling insights into how models make predictions.
Additional Learning Materials
Supplementary resources to enhance your learning experience.