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Check-In and Review

Suggested time: 5 min

Welcome back!

Take a few minutes to revisit the list of expectations your group established in the previous meeting. Open the floor to any suggestions from the group about additional expectations they’d like to propose going forward.

Module 2 and 3 Review

To recap, here are the main topics covered in earlier modules. We’ll build on these in Module 4.

Data is commonly used as another word for information. Oftentimes data is gathered in a specific format suitable for use on a computer (e.g., a spreadsheet). Data is a crucial ingredient in many artificial intelligence systems.

Learning algorithms are algorithms that determine their own procedure for a given task using data that we provide on previous inputs and desired outputs. Learning algorithms take the data we provide as experience and learn a mapping from input to output, often under constraints that we define.

Classifiers make predictions by assigning labels to observational data. In the video in Module 2, we saw several classifiers that observed facts about the world and decided whether the light should be turned on or off based on these observations. Classifiers are engineering artifacts. To check whether they work, we collect observations (e.g., whether it’s dark outside) for which we know what the outcome should be (e.g., whether the light is actually on), ask the classifier to make a prediction, and compare the prediction with the true outcome.

Stakeholders are individuals, groups, or organizations that may be affected by a decision, directly or indirectly. These effects can be positive or negative, depending on stakeholders’ values.

In the earlier modules, we discussed how machines learn from data to make decisions on our behalf, and how some of those decisions can have a great impact on our lives. This module explores how those decisions are influenced by human bias.

Group Check-In

Give everyone 1–2 minutes to share their responses to the following questions:

  • When you think about or hear the term “bias”, what comes to mind?
  • Considering your own experience or understanding of bias, how can you imagine bias positively or negatively impacting the data and technologies we’ve been exploring?

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