Impact of AI & Computing: every key term you need (+ practice quiz)

More AP Computer Science Principles guides

• Computing Systems & Networks
• Data & Representation
• Algorithms & Programming
• Programming & Modeling

Artificial Intelligence

Computer systems performing tasks requiring intelligence: learning, reasoning, problem-solving.

Machine Learning

AI subfield where algorithms learn patterns from data and improve through experience.

Neural Network

Machine learning model mimicking brain structure; layers of interconnected nodes learning patterns.

Deep Learning

Neural networks with multiple hidden layers enabling learning of complex patterns.

Supervised Learning

Training on labeled data (input-output pairs); algorithm learns to predict outputs.

Unsupervised Learning

Training on unlabeled data; algorithm discovers hidden patterns and groupings.

Reinforcement Learning

Algorithm learns through trial-and-error; rewards for good actions, penalties for bad.

Training Data

Dataset used to teach algorithm; larger, diverse datasets improve accuracy.

Overfitting

Model learns training data too well, including noise; performs poorly on new data.

Underfitting

Model too simple; fails to capture patterns; poor performance on all data.

Accuracy

Percentage of correct predictions; does not always indicate fair or representative model.

Precision

Among predictions classified as positive, how many were actually correct.

Recall

Among actual positives, how many did model identify correctly.

Generalization

Model performs well on new, unseen data; indicates good learning without overfitting.

Transparency

Ability to understand how an AI system makes decisions; important for accountability.

Explainability

Providing understandable explanations for AI decisions; why did model choose that?

Accountability

Responsibility for AI outcomes; who is responsible if algorithm fails or harms?

Ethical AI

Developing AI systems considering fairness, transparency, privacy, and human values.

AI Ethics

Field examining moral implications of AI: bias, privacy, autonomy, job displacement.

Autonomous Systems

AI systems making decisions and taking actions without human control.

Digital Divide

Unequal access to computing devices and the Internet based on income, geography, or demographics, which limits opportunity.

Computing Bias

Algorithms and models can reflect the biases of their creators or training data, producing unfair outcomes for some groups.

Crowdsourcing

Obtaining data, funding, or solutions from a large group of people online, e.g. citizen science or open mapping.

Citizen Science

Members of the public collect or analyze scientific data, often via apps, expanding what researchers can study.

Intellectual Property

Creations of the mind protected by law; copyright covers software and media, and using it without permission is infringement.

Creative Commons

Licenses that let creators specify how others may reuse their work, such as requiring attribution or forbidding commercial use.

Open Source

Software whose source code is freely available to use, modify, and redistribute under a license.

Personally Identifiable Information (PII)

Data such as name, address, SSN, or biometrics that can identify a person; aggregating it enables tracking and identity theft.

Cookies and Tracking

Small files and identifiers websites store to remember users and follow behavior across sites, raising privacy concerns.

Encryption

Transforming data so only someone with the key can read it; symmetric uses one shared key, public-key uses a key pair.

Public-Key Encryption

Uses a public key to encrypt and a matching private key to decrypt, allowing secure communication without pre-sharing a secret.

Certificate Authority

Trusted organization that issues digital certificates verifying a website's public key belongs to that site.

Multi-factor Authentication

Requiring two or more of something you know, have, or are, so a stolen password alone is not enough.

Phishing

Deceptive messages that trick users into revealing credentials or installing malware, often by imitating trusted senders.

Keylogger

Malicious software or hardware that records keystrokes to capture passwords and other sensitive input.

Rogue Access Point

Unauthorized wireless network set up to intercept traffic from users who connect to it.

Malware

Software designed to damage or gain unauthorized access, including viruses that attach to programs and spread.

Machine Learning

Programs that improve at a task by finding patterns in data; results depend heavily on the quality and representativeness of training data.