balance

AI Ethics & Responsible AI

Bias, fairness, transparency, accountability, and regulation — the principles and practices for building AI systems that are trustworthy, equitable, and aligned with human values
Co-Created by Kiran Shirol and Claude
Topics Bias & Fairness Transparency Privacy Regulation Governance Safety
home Learning Portal play_arrow Start Learning summarize Key Insights dictionary Glossary 10 chapters · 3 sections
Section 1

Foundations — Principles & Bias

Why AI ethics matters, how bias enters systems, and the frameworks for fairness.
1
warning
Why AI Ethics Matters
Real-world harms, the case for responsible AI, core ethical principles, and the cost of getting it wrong.
arrow_forward Learn
2
tune
Bias in AI Systems
Sources of bias (data, algorithmic, societal), types of bias, and real-world case studies of biased AI.
arrow_forward Learn
3
balance
Fairness Definitions & Metrics
Demographic parity, equalized odds, calibration, the impossibility theorem, and choosing the right metric.
arrow_forward Learn
4
build
Bias Mitigation Techniques
Pre-processing, in-processing, and post-processing methods. Fairlearn, AIF360, and practical debiasing.
arrow_forward Learn
Section 2

Transparency & Privacy

Explainability, interpretability, data privacy, and the right to an explanation.
5
visibility
Explainability & Interpretability
SHAP, LIME, attention visualization, model cards, and the trade-off between accuracy and interpretability.
arrow_forward Learn
6
lock
Privacy & Data Rights
GDPR, differential privacy, federated learning, data minimization, and the right to be forgotten.
arrow_forward Learn
7
smart_toy
LLM-Specific Ethics
Hallucination, copyright, deepfakes, RLHF alignment, and the unique ethical challenges of generative AI.
arrow_forward Learn
Section 3

Governance & Regulation

AI regulation, organizational governance, safety, and building ethical AI in practice.
8
gavel
AI Governance & Regulation
EU AI Act, NIST AI RMF, ISO 42001, corporate governance, and the global regulatory landscape.
arrow_forward Learn
9
diversity_3
Building Ethical AI Teams
Diversity, inclusive design, responsible AI culture, the 5 Ps framework, and participatory design.
arrow_forward Learn
10
rocket_launch
The Future of AI Ethics
AGI safety, alignment research, mechanistic interpretability, frontier AI governance, and emerging challenges.
arrow_forward Learn
link

Related Courses

neurology
Deep Learning Fundamentals
12 chapters · Foundations
deployed_code
MLOps & LLMOps
10 chapters · Production ML
model_training
Classic Machine Learning
8 chapters · Foundations