De Anza College Communications Essay

Choose any of the topics described in one of the datasets explored in Wed’s lab (see

https://drive.google.com/drive/folders/1EaQAUflXN499jrQjcgZPHdPs5fWETyVn

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or any other actual or hypothetical classification system you can imagine. For example:

  • Headlines that are clickbait vs not clickbait
  • Movie reviews that are positive vs negative
  • Tweets that are happy vs sad
  • Tweets that are offensive vs not offensive
  • College or job applications that will be accepted or rejected
  • Headlines that are “fake news” vs “real news”

Your response is to pick a topic for an AI classification system and 1) discuss a potential bias for/against particular groups, then 2) propose an approach to auditing it for that bias. For example, for our own happy/sad text classifier, I showed an approach to auditing for bias based on countries. I loaded a file with a list of all country names, then for each country in that list, I inserted it into a template sentence (“It is so nice in ” + country_name
+ “, I love it there!”) and scored it. I then showed that because I intentionally added pro-France and anti-Germany bias in the dataset, the sentence with France scores much higher than the sentence with Germany.

For the topic you choose, describe a kind of bias for/against particular groups you think is important to test for in that context, and how would you test for it? You would likely use the same general strategy of having a template sentence/text, but what examples would you insert? You do not have to implement this in code, just describe the general strategy. You are not allowed to pick bias based on countries, because that is what we did in class.

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