Ethics & Safety

38

What to watch out for when using AI. Bias, deepfakes, prompt injection, alignment, copyright, privacy: concepts that are all over the news yet hard to pin down.

Level
Accountability

Figuring out who has to answer for what an AI produces

Intro
AGI

AI that could learn any new task, not just the one it was built for

Basics Basics
AI & Copyright

The open questions of rights around AI training and AI output

Intro
AI Art

Pictures made with generative tools, and the debates around them

Generative Intro
AI Bias

Skew in the data and how it's built tilting an AI's judgment one way

Intro
AI Content Detection

Working out whether something was made by an AI

Intro
AI Ethics Dilemma

Several reasons worth honoring, but not all can be followed

Intro
AI Regulation

Rules that set how far and how AI can be used

Intro
AI Watermark

An unobtrusive mark embedded inside what AI generates

Basics
Alignment

Matching what an AI is capable of to what people actually want from it

Basics
API Key

The ID you present when you call an API

Tools Intro
Black Box

An AI's decision process when the inside can't be seen

Intro
Content Credentials

A signed record of capture and editing history attached to a file

Basics
Dataset Bias

When the data you collected already leans to one side

Evaluation Intro
Deepfake

Fake video or audio where AI copies a real face or voice

Intro
Environmental Cost

The electricity and resources it takes to build and run AI

Intro
Explainability

How much an AI can show for why it decided what it did

Basics
Face Recognition

Comparing faces to work out who someone is

Vision Intro
Fairness

The standard for whether AI treats people the right way

Intro
Federated Learning

Skipping the data pool, gathering only what each side learned

Training Advanced
Grounding

Tying each sentence of an answer to real evidence

LLM Basics
Guardrails

The safety fence set up in advance to keep AI from crossing a line

Intro
Hallucination

When an AI invents an answer that sounds right but is not true

LLM Intro
Human in the Loop

Putting a person's judgment at key points in an AI workflow

Intro
Jailbreak

Getting AI to talk its way past the rules it's supposed to follow

Basics
Misinformation

Something untrue spreading around as if it were true

Intro
Model Card

The document that spells out what a model can and can't do

Tools Basics
Model Collapse

When AI keeps learning from its own output, variety quietly shrinks

Advanced
Over-Reliance

When you lean on it so much that wrong answers go uncaught

Intro
Privacy

Handling information that lets someone be identified

Intro
Prompt Injection

An attack that slips instructions into AI input to bend its rules

Basics
Red Teaming

Trying to break it on purpose, before release and after changes

Basics
Refusal

An AI receiving a request and answering that it won't do it

Intro
Robustness

Still holding up when conditions get shaken

Evaluation Basics
Sampling Bias

A skew in data caused by who got picked

Evaluation Basics
Speaker Recognition

Telling who spoke from the sound of their voice

Vision Basics
Sycophancy

The habit of agreeing with the user even when they're wrong

Basics
Voice Cloning

Imitating a person's voice from a short recording

Vision Basics