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.
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.
Figuring out who has to answer for what an AI produces
AGIAI that could learn any new task, not just the one it was built for
AI & CopyrightThe open questions of rights around AI training and AI output
AI ArtPictures made with generative tools, and the debates around them
AI BiasSkew in the data and how it's built tilting an AI's judgment one way
AI Content DetectionWorking out whether something was made by an AI
AI Ethics DilemmaSeveral reasons worth honoring, but not all can be followed
AI RegulationRules that set how far and how AI can be used
AI WatermarkAn unobtrusive mark embedded inside what AI generates
AlignmentMatching what an AI is capable of to what people actually want from it
API KeyThe ID you present when you call an API
Black BoxAn AI's decision process when the inside can't be seen
Content CredentialsA signed record of capture and editing history attached to a file
Dataset BiasWhen the data you collected already leans to one side
DeepfakeFake video or audio where AI copies a real face or voice
Environmental CostThe electricity and resources it takes to build and run AI
ExplainabilityHow much an AI can show for why it decided what it did
Face RecognitionComparing faces to work out who someone is
FairnessThe standard for whether AI treats people the right way
Federated LearningSkipping the data pool, gathering only what each side learned
GroundingTying each sentence of an answer to real evidence
GuardrailsThe safety fence set up in advance to keep AI from crossing a line
HallucinationWhen an AI invents an answer that sounds right but is not true
Human in the LoopPutting a person's judgment at key points in an AI workflow
JailbreakGetting AI to talk its way past the rules it's supposed to follow
MisinformationSomething untrue spreading around as if it were true
Model CardThe document that spells out what a model can and can't do
Model CollapseWhen AI keeps learning from its own output, variety quietly shrinks
Over-RelianceWhen you lean on it so much that wrong answers go uncaught
PrivacyHandling information that lets someone be identified
Prompt InjectionAn attack that slips instructions into AI input to bend its rules
Red TeamingTrying to break it on purpose, before release and after changes
RefusalAn AI receiving a request and answering that it won't do it
RobustnessStill holding up when conditions get shaken
Sampling BiasA skew in data caused by who got picked
Speaker RecognitionTelling who spoke from the sound of their voice
SycophancyThe habit of agreeing with the user even when they're wrong
Voice CloningImitating a person's voice from a short recording