Faysal AzizFA-0521Agentic & Generative AIDevOps, Cloud & Infrastructure
AI Safety & Ethics
From Circuits to Secrets in a day
Introduction
Why this course
Understand how AI works, why it’s unpredictable, and how to keep it safe in a world that’s playing catch-up.
AI is no longer a niche research field—it’s a mainstream force shaping politics, industry, and everyday life. Yet the people building and regulating it often understand it only partially. This course blends an accessible technical primer with a candid look at geopolitical tensions, regulatory loopholes, and ethical dilemmas. Led by an instructor with over 30 years of industry experience, we’ll combine serious insights with engaging, relatable examples—from AI-driven global rivalries to deepfake mischief—to explore how to use AI responsibly in the real world.
Learning outcomes
Learning outcomes
By the end of the day, participants will:
- Understand the basics of how AI systems—especially large language models (LLMs)—are trained and operate.
- Explain why AI behaves unpredictably and remains a “black box” despite impressive capabilities.
- Describe key global AI political and regulatory positions, including the EU’s military exemption and the Trump administration’s deregulatory stance.
- Recognize the role of U.S.–China AI rivalry in shaping technology access and standards.
- Evaluate the ethical and legal challenges of AI training data collection without consent.
- Identify threats from deepfakes and apply safety measures to avoid being fooled.
- Engage critically and creatively with AI ethics issues through interactive activities.
Prerequisites
Prerequisites
- Basic familiarity with AI concepts and current events.
- No coding knowledge required—just curiosity and critical thinking.
Training outline
7 modules
·
011. AI 101: Under the Hood3 topics
- What AI Really Is:
- Narrow AI vs. General AI.
- Neural networks, machine learning, and deep learning—how they differ.
- How AI Learns:
- Data collection, preprocessing, and labeling.
- Training using backpropagation and gradient descent.
- Role of GPUs/TPUs in computation.
- Large Language Models (LLMs):
- Tokenization, embeddings, attention mechanisms, and transformers.
- The scaling laws that made modern AI possible.
- Why bigger data + more compute ≠ always better results.
022. AI’s Mystery Sauce4 topics
- The Black Box Problem:
- Why models can solve problems without us fully knowing how.
- Complex feature representations that humans can’t intuitively interpret.
- Geoffrey Hinton’s Concerns:
- His suspicion of backpropagation’s long-term viability.
- Calls for rethinking AI foundations before unexpected failures emerge.
- Non-Determinism in Action:
- Why repeated prompts to the same model can produce different outputs.
- Impact on safety, reliability, and accountability.
- Implications:
- Limits of interpretability tools like SHAP and LIME.
- Risk assessment in high-stakes environments.
- Overview of recent AI safety incidents: deceptive LLM behavior and evaluation shortcomings
033. Geopolitics & Regulation4 topics
- Key global initiatives:
- EU's AI Act (in force since August 1, 2024) and applicability timelines
- UNESCO’s AI Ethics recommendations
- California’s policy report on irreversible AI risks and trust‑but‑verify approach
- Other governance efforts (AI Safety Institutes, global summits)
- EU’s Double Standard: Civilian AI oversight vs. military exemptions.
- Trump’s Deregulation Push: Prioritizing AI dominance over safety rules.
- U.S.–China Cold War: Chips, data, and digital infrastructure supremacy.
044. Data Ethics3 topics
- The Consent Problem: How most LLMs used public data without permission.
- Legal Grey Zones: GDPR’s “legitimate interest” loopholes vs. privacy rights.
- Cultural Impacts: Artists, journalists, and domain experts losing control of their work.
055. Deepfakes4 topics
- How They Work: GANs, diffusion models, and real-time manipulation.
- Real Dangers: Scams, political disinformation, revenge porn.
- Personal Safety: Verification habits, detection tools, skepticism training.
- Fun Demo: Light-hearted but safe deepfake examples to illustrate risk without harm.
066. Building Ethical Assurance into AI Systems3 topics
- Frameworks for ethics‑based auditing and continuous oversight.
- Implementing transparency, explainability, accountability, and governance layers aligned with best practices and standards.
- Discussion: evaluating tools like
- HELM Safety,
- AIR‑Bench,
- FACTS for factuality and safety testing
077. Interactive Wrap-Up3 topics
- Scenario Game: Choose your role—regulator, engineer, activist, or everyday citizen—and craft a mini AI safety plan.
- Meme-Making: Teams create ethical memes from course takeaways.
- Group reflection on AI’s future and where responsibility truly lies.
A programme built around your team.
Share your training goals and requirements.