Overview
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Learn why it's absolutely crucial for AI-related data science work to be transparent, explainable, accountable, and ethical in its design and execution.
Syllabus
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Introduction
- Accountable artificial intelligence
- The promise of AI
- General and narrow AI
- The challenge of classification errors
- The causes of classification errors
- Bias in AI
- Supervised and unsupervised learning
- Biased labeling of data
- Construct validity
- Seeing the wrong thing
- The absence of meaning
- Vulnerability to attacks
- Attacking AI
- Protecting AI
- Dimensions of justice
- Justice and AI
- Moral reasoning
- Relational ethics
- Issues of authenticity
- General Data Protection Regulation (GDPR)
- Spurious discrimination
- The right to explanation
- Discrimination in data
- Discrimination in implementation
- Liability
- Accountability laws
- AI in life and death situations
- AI in the military
- The challenges of military AI
- Strategies for developers
- Strategies for executives
- Strategies for public relations
- Strategies for regulators
- Strategies for consumers
- Next steps
