Big Idea 5: Impact of Computing

Computing innovation

A new or improved computer-based product or system (often combining hardware, software, and data) that changes how people live, work, or communicate.

Trade-off

A situation where a computing innovation creates benefits and harms at the same time, requiring a balance between competing outcomes (e.g., convenience vs. privacy).

Unintended consequence

An impact of a computing innovation that was not originally planned or foreseen by its designers, often emerging after widespread use.

Stakeholder

A person or group affected by a computing innovation, such as individuals, communities, organizations, governments, or the environment.

Beneficial effect

An outcome of a computing innovation that improves quality of life, access, safety, productivity, or knowledge.

Harmful effect

An outcome of a computing innovation that creates risk, inequity, damage, exploitation, or loss (including privacy loss).

Direct effect

An immediate, intended result of a computing innovation (e.g., video conferencing enabling remote meetings).

Indirect effect

A secondary consequence that emerges later due to adoption of an innovation (e.g., remote work changing commuting patterns and local business revenue).

Network effects

When an innovation becomes more valuable as more people use it, often driving rapid adoption and sometimes market concentration.

Algorithm (ranking/recommendation)

Software logic used to sort, rank, or recommend content (such as posts or videos), strongly shaping what users see and how information spreads.

Cloud computing

Storing and processing data on remote servers (not on the user’s local device), enabling easier access, sharing, and collaboration over the internet.

Server

A computer that stores data and/or provides services to other computers over a network; in cloud storage, files are stored on remote servers.

Open access data

Publicly shared data made available by organizations (such as governments) so anyone can search, analyze, and use it to solve problems or create innovations.

Public database

A large organized collection of data that is made available for public use in fields like science, sports, entertainment, and business.

Analytics

The use of collected data to identify patterns and trends (often for marketing), such as what people search for, click on, or buy and when.

Search trends

Aggregated information about what topics or queries are being searched or posted about most frequently, sometimes published by platforms.

Targeted advertising

Ads aimed at specific users based on collected and analyzed user data; can help consumers find relevant items but increases privacy risks.

Data

Facts or measurements collected for reference or analysis; in computing, data collection can shift power toward those who control the data.

Personally identifiable information (PII)

Information that identifies a person directly or indirectly, including obvious identifiers (name, address) and sensitive data (medical or financial info).

Inference

Deriving sensitive facts or identifying people from data that may not explicitly include identifiers (e.g., using location points to find home/work).

Explicit data collection

Data a user intentionally provides, such as information entered in sign-up forms or surveys.

Implicit data collection

Data collected automatically from user behavior or devices, such as clicks, watch time, location, or device identifiers.

Digital footprint

The trail of data left by online activity (posts, messages, logins, photos, metadata), which is often easy to copy and hard to fully erase.

Incognito/private browsing mode

A browser mode intended to prevent searches and downloads from being saved in that device’s local history (not a guarantee of total tracking prevention).

Personalization

Using collected data to tailor content or recommendations to an individual user, improving convenience but potentially reducing privacy.

Automated decision-making

Using data and algorithms to make or assist decisions (e.g., hiring filters, loan approvals), which can increase efficiency but risk unfair outcomes.

Anonymization

Removing personal identifiers from a dataset to reduce privacy risk; it lowers risk but does not guarantee privacy.

Re-identification

Matching “anonymous” data to real people by linking it with other datasets (e.g., using a few location/time points to identify someone).

Surveillance

Monitoring behavior or activities at scale using computing (e.g., cameras, face recognition, location tracking), with both safety benefits and abuse risks.

Data breach

An incident where private data is accessed or exposed without authorization, potentially causing long-term privacy loss and other harms.

Identity theft

A harm from exposed personal data where someone uses another person’s information to commit fraud (often following a breach).

Digital divide

Unequal access to devices, internet, and the knowledge needed to use computing effectively, affecting who benefits from technology.

Access divide

A layer of the digital divide focused on who has reliable devices and internet connections (including quality and reliability).

Use divide

A layer of the digital divide focused on differences in skills, time, and support needed to use technology effectively.

Outcome divide

A layer of the digital divide describing unequal benefits from technology in areas like education, jobs, and health.

Accessibility

Designing computing innovations so people with disabilities can perceive, understand, navigate, and interact with them effectively.

Assistive technology

Hardware or software that helps people perform tasks (e.g., screen readers, captions, voice typing), often benefiting many users beyond the target group.

Inclusive design

Planning for diverse users from the start (including accessibility needs) rather than retrofitting later, which is often more expensive and incomplete.

Bias (in computing)

A systematic tendency toward certain outcomes in computing systems, which can be intentional or unintentional and can produce unfair results.

Underrepresentation

When some groups appear less in a dataset, leading models to work better for the majority and worse for underrepresented groups.

Feedback loop

When a system’s outputs influence future inputs, reinforcing patterns over time (e.g., recommendations increasing views, which increases recommendations).

Transparency

The extent to which a system’s data use and decision processes are understandable and explainable to affected people and reviewers.

Accountability

The expectation that people or organizations can be held responsible for a system’s outcomes, including oversight and evaluation in high-stakes uses.

Crowdsourcing

Getting contributions (data, ideas, labor, money, or computing power) from a large group of people, typically via the internet.

Microtasking (human computation)

A type of crowdsourcing where people perform small tasks computers struggle with, such as labeling images or transcribing audio.

Intellectual property

Creations of the mind (including software and other computational artifacts) that belong to their creators and can be legally protected.

Copyright

A legal protection giving creators rights over how their work is used and distributed; “online” does not mean “free to use without permission or citation.”

Creative Commons

A licensing approach that lets creators share work with clear conditions (e.g., requiring attribution or limiting commercial use).

Cybersecurity

Protecting devices, networks, and data from unauthorized access or damage; it has global impact because attackers can act from anywhere.

Phishing

A social-engineering attack using emails or websites that look legitimate to trick people into clicking malicious links or revealing credentials.