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# System prepended metadata

title: Lecture 18 Setup/Prep
tags: [Setup]

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tags: Setup
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# Lecture 18 Setup/Prep

What makes a program or algorithm good? We asked you this on the background survey at the start of the course, and most of you focused on issues like execution speed, space consumption, and clarity. A few of you mentioned that we should also think about issues like fairness or social impact.

How do we measure an algorithm for social impact? Can we do anything systematic like we do for run-time and space usage? 

This lecture will focus on assessing social impact of algorithms and programs in systematic ways. We'll look at two case studies through a specific set of guidelines that we're developing right here at Brown CS.  You'll spend time working on the cases in discussion groups (putting some responses into google docs), with broader/class-wide discussion interleaved. You'll get to give feedback on an ongoing research project here at Brown CS on how to teach this material in intro courses. 

You'll work with this same set of guidelines on projects/assignments later in the semester, so this isn't a throw-away lecture. But it will give us a breather from new coding content at a hectic time during the semester for all of us.

## Prep

Nothing to prepare. Just come to class.