Teaching Ethics in Data Science for High School Math

The most common mistake my high school students make when we talk about digital privacy is assuming that a privacy policy is just legal jargon meant to be ignored. They see a wall of text and click accept without thinking twice. We can teach students that these documents are functional, legal statements explaining how an organization collects, uses, and shares personal data through a structured lesson on ethics.

Rethinking the privacy policy

In this high school mathematics lesson, Ethics in Data Science, students are forced to look at those documents as they actually function. They learn that a privacy policy is a legal statement explaining how an organization collects, uses, and shares personal data.

$An algorithm denies insurance to people based on their zip code, even though this unfairly punishes some groups. This is not.$

When I taught this in a Precalculus Data Science context, we started with a clear example from the material. If a social media app requests information from 100 people, the policy reveals exactly what happens to the 5 data points collected from each individual. By shifting the perspective from passive user to analytical observer, students begin to see that these policies are not just fluff. They are the rules of the game. Teachers in the Kern High School District, Riverside Unified, Morongo Unified, and Santa Monica-Malibu Unified all recognize this as a foundational step for data ethics high school math curriculum across California.

Looking at the machine’s blind spots

Once we moved past the basics of privacy, the lesson challenged the class to consider what happens when math is used to make human-centered decisions. We discussed algorithmic bias, which is when a computer program produces unfair outcomes due to flaws in its design or the data used to teach it. My students usually assume that because a computer calculated the result, it must be objective. That is a dangerous assumption to hold when analyzing datasets.

The lesson uses the example of a bank evaluating loan applicants to show the necessity of active monitoring. If a bank monitors its algorithms to ensure that approvals are consistent across different neighborhoods, it is practicing ethical data management. It accepts responsibility for the impact its decisions have on all applicants. This specific, concrete example helps students realize that behind every algorithmic output there is a design choice that carries real consequences for individuals. This is a core expectation of the standard CA.MATH.HS.DS.13, which covers the various ethical considerations in data science practice.

Moving from private profit to public good

I find that students become most opinionated when we transition to the guided practice portion of the lesson, which focuses on Public Sector Responsibility. We compared corporate goals to the requirements of government entities, such as city traffic routing systems. Unlike a company that might focus primarily on profit, a public sector model has to prioritize equity and the public good. The lesson explains that these models are held to higher standards of transparency and civic accountability because they serve the entire population.

$A website collects user data without a clear policy, leaving users confused about their privacy. This is not ethical because.$

We analyzed a scenario where a city uses an app to route traffic. We asked ourselves: is the fastest route for one driver actually fair for the neighborhood they are driving through? This forces students to define what fairness looks like when translated into a mathematical model. It teaches them that data scientists have a duty to consider whether their work serves the entire population regardless of location or economic status. This is a great way to handle an independent study or a substitute day where you want students to grapple with high-level conceptual thinking without needing constant direct instruction.

Why this matters in the math classroom

By the time we reached the final vocabulary term, Personally Identifiable Information, the class understood why this topic belongs in a math course. Data is not just numbers. It consists of names, email addresses, and identification numbers that belong to real people. The lesson argues that these models influence lives, from the jobs people get to the healthcare they receive. Ethical data science ensures that a person is treated as a human being rather than just a data point.

I suggest using this lesson for a whole-class assignment when you have reached the data science portion of your curriculum. It is also excellent for a student who missed the week, as it stands on its own and requires no complex prerequisites. If you find your class struggling to connect abstract statistics to their own lives, use the bank loan example to bridge that gap. The lesson material is grounded in the reality that our students are data subjects, and they should know how their information shapes the world they deal with every day.