IL Tutorial Series #3: Algorithms & Filter Bubbles

Algorithms and Filter Bubbles

This module will define and discuss how algorithms shape our online searches, and how filter bubbles may affect our search results. By the end of the module, you will be able to:

• understand how algorithms rank and personalize search results
• define "filter bubble" and understand their impact on search results
• identify some of our own behaviors that influence our search results

Read the two short articles under Module Readings and the comic below, then watch the video "Beware Online Filter Bubbles" on this page.

Then scroll down to answer the quiz questions.

The Markup. (n.d.). How different are Americans’ Facebook feeds? [Interactive graphic]. https://themarkup.org/citizen-browser/2021/03/11/split-screen?feed=biden_trump

Kwok, R. (2021, November 24). Can bias be eliminated from algorithms? [Interview with Soheil Ghili]. Yale Insights. https://insights.som.yale.edu/insights/can-bias-be-eliminated-from-algorithms

The Life in the Age of Algorithms infographic, seen above, shows the following information:

• Data are everywhere, collected 24/7, and processed in real-time and at vast scale.
• Platforms seek patterns in data to try to predict and influence behavior, and personalize news and advertising, based on guesses about individual preferences, and creating the potential for us to experience different realities.
• Our lives are increasingly influenced by decision-making systems that build on correlations, using "artificial intelligence" software that relies on incomplete data to make generalizations, and amplifies existing biases at large scale.
• Information systems we depend on are shaped by a tech culture marked by narrow perspectives and overconfidence, that rely on mining users' data and manipulating their behavior, in ways that undermine our trust in news, politics, and each other.