Reading Comprehension · Grade 12

Name Date

How Algorithms Shape What You See

Science · 5 questions · 536 words

1 Every time someone opens a video app, a music service or a social feed, a piece of software has already made hundreds of small decisions about what to show first. These programs are called recommendation systems, and their basic task is simple to state but difficult to perform: out of millions of possible posts, songs or clips, select the handful that one particular person is most likely to watch, play or click at that moment. Because no human editor could sort so much material for every user, the sorting is done by algorithms, sets of step-by-step instructions that a computer follows to reach a decision.

2 A recommendation system begins by collecting signals, the traces people leave behind as they use a service. Some signals are deliberate, such as a like, a follow or a search. Others are quieter, such as how long someone watched a clip before scrolling past it, whether a song was replayed, or what time of day a person usually visits. The system combines these signals with information about the items themselves, such as a video's subject or length, and with patterns drawn from millions of other users. One widely used method, called collaborative filtering, rests on a principle that sounds almost like neighborly advice: people who agreed with you in the past will probably agree with you again, so the items they enjoyed are suggested to you. From all of this, the system predicts a score for every possible item, ranks the items from highest to lowest, and displays the top of the list.

3 What the score is designed to measure matters enormously. Many systems are built to predict engagement, meaning the likelihood that a person will click, watch, share or comment. Engagement is easy to count, and companies often treat it as evidence that users are satisfied, but the two are not always the same. A post that makes people angry may attract hundreds of comments without leaving anyone better informed, and a video that keeps someone watching past midnight may not be one they are glad to have seen. For this reason, some companies also survey users directly about how much they valued what they saw and blend those answers into the score.

4 Recommendation systems also create feedback loops. If a viewer watches three cooking videos, the system offers more cooking videos, which the viewer is then more likely to watch, which in turn seems to confirm the system's original prediction. Some critics argue that loops like this gradually narrow what people encounter, enclosing them in a "filter bubble" of familiar ideas. Researchers disagree about how strong this effect is in practice. Some studies have found that many users still meet a fairly wide mix of sources, partly because of what they search for themselves and what other people share with them.

5 Understanding this machinery does not require distrusting it, but it does offer a measure of control. Because a recommendation system learns from behavior, a user's choices become part of its instructions. Searching for unfamiliar subjects, pressing a "not interested" button, clearing a viewing history, or simply pausing to ask why a suggestion appeared are all ways to influence what an algorithm decides a person wants to see next.

Written for PlayOwl

Questions

Read the passage. Fill in the circle next to the best answer. Name the paragraph that proves each answer.

  1. 1Which statement best expresses the central idea of the passage?

    • The systems rank items from people's signals, and what they are built to predict shapes what people see.
    • Recommendation systems trap nearly every one of their users inside a filter bubble of familiar ideas and sources.
    • Human editors now choose most of what appears in video apps and social feeds.
    • Collaborative filtering is the only method a recommendation system needs in order to work.

    Evidence: paragraph

  1. 2How does paragraph 3 develop the meaning of the term "engagement"?

    • It uses the term as another name for how satisfied users feel.
    • It defines the term as measurable action, then shows it can differ from what people value.
    • It treats the term as a measure of how accurate and trustworthy the information in a post is.
    • It narrows the term to mean only the comments people leave under a post.

    Evidence: paragraph

  2. 3Based on paragraph 4, why might a feedback loop make a system's predictions look better than they really are?

    • Viewers secretly prefer cooking videos to every other kind of content.
    • The system quietly deletes any prediction that turns out to be mistaken, so only accurate ones remain.
    • Researchers check each prediction before it reaches the viewer.
    • The system can partly cause the viewing that it then counts as proof it was right.

    Evidence: paragraph

  3. 4The passage leaves one question about recommendation systems unsettled. Which sentence shows this most clearly?

    • "Recommendation systems also create feedback loops."
    • "Researchers disagree about how strong this effect is in practice."
    • "Engagement is easy to count, and companies often treat it as evidence that users are satisfied …"
    • "Because a recommendation system learns from behavior, a user's choices become part of its instructions."

    Evidence: paragraph

  4. 5Why does the author end the passage with paragraph 5?

    • To warn readers to stop using apps that recommend content
    • To admit that the earlier explanation of recommendation systems was mistaken
    • To show that knowing how the systems learn gives users some influence over them
    • To argue that companies should stop measuring engagement and rely on surveys alone

    Evidence: paragraph