<aside> ⌛ Estimated student time on platform: 45 minutes (+ blending)

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<aside> 🗣 Lesson host: Nicco Mele, Shorenstein Center on Media, Politics and Public Policy, Harvard Kennedy School.

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<aside> 🔢 Difficulty level: 1.5 (for middle school, high school and higher education)

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<aside> ✔️ Assessments: 5 total ****(all teacher-evaluable)

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<aside> 🗒️ Standards: This lesson has Common Core, ISTE, C3 and state-specific alignments. Find your standards in the Checkology alignments dropdown menu to learn more.

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Learning objectives:

Essential questions:

Background:

Personalization is so ubiquitous in today’s information ecosystem that it is often rendered transparent. It can be difficult for anyone to detect when their information is being shaped and filtered by algorithms — and especially for students, who have likely always had their information sorted and served that way. The exact effect of algorithms on a specific online experience is often impossible to determine.

This lesson gives students a basic introduction to the concept of algorithmic personalization and helps them to reflect on its advantages and potential pitfalls.

Most people have grown so accustomed to algorithms tailoring their information that it can be challenging for them to understand what a completely unfiltered online experience might be like. For example, the algorithms that drive search engines detect both the language and the country of origin of a given search and take those factors into account when serving results. But few people likely consider this fact; nor do they consider what it would be like to have to sift through a set of entirely raw search results.

Search engine algorithms use far more than just these basic details to determine the relevance of search results. They count how many other pages link to a given page to help guess its importance and quality. They track how frequently different links are clicked after specific searches and use that data to refine future results. And they use our previous search and browsing histories to learn about who we are (for example, our age, gender and marital status, as well as our interests and habits), then factor that information into their determination of which results would be most relevant — not just to our search terms, but to us as well.

Algorithms also frequently use the location of our IP addresses or our mobile devices to further tailor results, allowing users to search for the nearest grocery store or gas station or find the quickest route to their destination. Algorithms on social media platforms promote, or “privilege,” posts by people with whom we frequently interact, and they use the language and tone of our online comments and other posts to make guesses about our personalities. On some news sites, algorithms try to guess which stories will interest us and suggest them in some way.

And often, the same data generated by search engines, social media platforms and other free services are used to target us with advertising. In fact, that’s the entire business model of the “ad tech” sector: providing free online services to gather important data that can be used to provide advertisers with very specific audience segments to whom they can tailor marketing messages.

There are other downsides. Algorithms learn about our political beliefs and can wind up codifying our biases and strengthening our blind spots. They often return search results that reflect and affirm our existing ideas and opinions instead of confounding or challenging them, thus possibly driving people to take ideological positions that are more extreme. They can contain the race and gender biases of their creators. They can act without our knowing, wresting control of our information experiences away from us and placing them in the hands of others. They can amass a shocking amount of data that, if ever put together, would violate people’s expectations of privacy.

As a basic introduction to these very complex and rapidly changing trends, this lesson gives students a foundational understanding of the advantages and challenges of algorithms and personalization that can help drive their awareness and guide further inquiry into this fascinating and important topic.


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Full lesson guide: Introduction to Algorithms

From the Field: Introduction to Algorithms