filter bubble
In plain wordsA situation in which you see mostly news and opinions you already agree with, because apps choose what to show you based on what you clicked before.
The hypothesized narrowing of what a person encounters online when personalization algorithms, such as recommender systemsrecommender system, n.: Software that guesses which songs, products, posts or jobs you are most likely to want and shows those first, as when a video app lines up the next clip or a shop suggests an add-on. and personalized search, choose content from their past behavior, so that they meet ever more belief-confirming material without knowing what has been left out. It is related to, but distinct from, an echo chamber, which arises from people's own choices of sources and communities. Empirical evidence is mixed: large studies generally find that algorithmic curation narrows exposure to opposing views less than users' own choices do, that most people's media diets are more varied than the metaphor suggests, and that strong effects appear mainly in small, highly engaged groups. The concern has extended to chatbots that personalize answers or flatter users' existing views (sycophancysycophancy, n.: When an AI tells people what they want to hear — flattering them, agreeing with their mistakes or dropping a correct answer under pushback — instead of what is true or useful.).
The study found less of a filter bubble than feared: the feed showed opposing views, which users mostly skipped.
Origin
Coined by internet activist Eli Pariser in The Filter Bubble: What the Internet Is Hiding from You (2011) and a TED talk the same year, which pointed to personalized Google results and Facebook's news feed. Earlier versions of the worry include Nicholas Negroponte's 'Daily Me' (1995), developed by legal scholar Cass Sunstein.
Usage note
Chinese discussion uses 'information cocoon', a phrase popularized through translations of Sunstein, for a similar concern.
Related
- recommender systemrecommender system, n.: Software that guesses which songs, products, posts or jobs you are most likely to want and shows those first, as when a video app lines up the next clip or a shop suggests an add-on.
- engagement optimizationengagement optimization, n.: Designing apps and AI to keep people clicking, watching or chatting for as long as possible because that earns money, even when it is not what is good for them.
- homogenizationhomogenization, n.: The tendency of AI to make things more alike: products built on the same few models share the same flaws, and people who write or create with AI produce more similar work.
- misinformationmisinformation, n.: False or misleading information that spreads, whether or not the people sharing it know it is wrong. AI can produce it by mistake and makes it cheaper to create.
- sycophancysycophancy, n.: When an AI tells people what they want to hear — flattering them, agreeing with their mistakes or dropping a correct answer under pushback — instead of what is true or useful.
Sources
- Pariser, E. (2011). The Filter Bubble: What the Internet Is Hiding from You. Penguin Press.
- Bakshy, E., Messing, S. & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science 348(6239), 1130–1132.
Mentioned in
misinformationmisinformation, n.: False or misleading information that spreads, whether or not the people sharing it know it is wrong. AI can produce it by mistake and makes it cheaper to create., recommender systemrecommender system, n.: Software that guesses which songs, products, posts or jobs you are most likely to want and shows those first, as when a video app lines up the next clip or a shop suggests an add-on., engagement optimizationengagement optimization, n.: Designing apps and AI to keep people clicking, watching or chatting for as long as possible because that earns money, even when it is not what is good for them., feedback loopfeedback loop, n.: A cycle in which what a system produces comes back to shape its own future input, like a microphone howling near its loudspeaker. In AI, a model's predictions can shape the very data it later learns from., homogenizationhomogenization, n.: The tendency of AI to make things more alike: products built on the same few models share the same flaws, and people who write or create with AI produce more similar work.