Computer Science / Artificial Intelligence

AI Accountability Essential Training

بواسطة: LinkedIn Learning

Overview

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

Introduction

Accountable artificial intelligence

1. The Context for AI

The promise of AI
General and narrow AI

2. Technical Challenges of 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

3. Social Challenges of AI

Dimensions of justice
Justice and AI
Moral reasoning
Relational ethics
Issues of authenticity

4. Legal Challenges of AI

General Data Protection Regulation (GDPR)
Spurious discrimination
The right to explanation
Discrimination in data
Discrimination in implementation
Liability
Accountability laws

5. Safety Challenges of AI

AI in life and death situations
AI in the military
The challenges of military AI

6. Confronting the Challenges of AI

Strategies for developers
Strategies for executives
Strategies for public relations
Strategies for regulators
Strategies for consumers

Conclusion

Next steps

Taught by

Barton Poulson

Computer Science / Artificial Intelligence

AI Accountability Essential Training

الذهاب الي الدورة

AI Accountability Essential Training

بواسطة: LinkedIn Learning

LinkedIn Learning
مدفوعة
الإنجليزية
متاح شهادة
متاح في أي وقت
الجميع
N/A

Messages Timeline Exceptions Views14 Route Queries6 Models5 Gate Session Request

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