Generative AI, Ethics & Final Revision Checklist

Let’s close out this series with two commonly tested topics — Generative AI and AI Ethics — plus a consolidated checklist for last-minute revision before the CCAT exam.

What Is Generative AI?

Generative AI refers to systems that create new content — text, images, audio, video, or code — that resembles but is distinct from their training data. This is different from purely predictive/classification AI, which only labels or scores existing input.

Key Technologies Behind Gen AI

Technology	Generates	Examples
Large Language Models (LLM)	Text	GPT, Llama
Diffusion Models	Images	DALL-E, Stable Diffusion
GANs (Generative Adversarial Networks)	Realistic images/video	Deepfakes, synthetic media

GAN structure to remember: A GAN has two competing networks — a Generator (creates fake content) and a Discriminator (tries to detect fakes). They train against each other until the Generator produces convincing output.

The Gen AI Pipeline

Training Data → Transformer Model → Fine-tuning (RLHF) →
Prompt → Generated Output

RLHF = Reinforcement Learning from Human Feedback — this is how models like ChatGPT are fine-tuned to align with human preferences after initial training.

Risks of Generative AI — A Frequently Tested List

Risk	Description
Deepfakes	Fake videos/images of real people
Misinformation	AI-generated false content
Hallucination	Confidently generating incorrect facts
Bias	Reflecting biases present in training data
Copyright issues	Training on copyrighted content without consent

AI Ethics — The Six Major Concerns

Concern	Key Example
Bias & Fairness	Amazon’s scrapped hiring AI (gender bias); facial recognition error rates varying by skin tone
Privacy	Mass surveillance via facial recognition
Accountability	Who’s liable for a self-driving car accident?
Transparency	“Black box” decisions; EU GDPR Article 22 grants a “right to explanation”
Job Displacement	Automation affecting routine roles
Autonomous Weapons	AI weapons operating without human oversight

Exam trap: “Explainable AI (XAI)” is the research field specifically addressing the transparency concern — don’t confuse it with bias mitigation, which is a separate research area.

Final Revision Checklist

Use this as your last-minute run-through before the exam:

• AI ⊃ ML ⊃ DL hierarchy and the “not all AI is ML” exception
• Supervised (labeled) vs. Unsupervised (unlabeled) vs. Reinforcement (reward-based)
• Logistic Regression = classification, despite the name
• Perceptron can’t solve XOR; MLP can
• CNN = images (convolution + pooling); RNN/LSTM = sequences (hidden state, vanishing gradient)
• Transformers use self-attention, not recurrence
• NLP pipeline: Lexical → Syntactic → Semantic → Discourse → Pragmatic
• NLU = understanding, NLG = generation
• Classification vs. Detection vs. Semantic vs. Instance Segmentation
• GAN = Generator + Discriminator
• Key dates: 1956 (Dartmouth), 1958 (Perceptron), 1997 (Deep Blue), 2016 (AlphaGo), 2017 (Transformer)
• Six major AI ethics concerns: bias, privacy, accountability, transparency, job displacement, autonomous weapons

Closing Thought

CCAT-style AI questions reward precision over general familiarity — the exam is less about “do you know AI is a big field” and more about “can you correctly distinguish similar-sounding terms under time pressure.” Going back through this series with the comparison tables and traps highlighted should give you a strong, exam-ready foundation.

Good luck with your preparation!