Another dot in the blogosphere?

CrashCourse AI episode 18

Posted on: December 15, 2019

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This was an episode that anyone could and should watch. It focused on bias and fairness as applied in artificial intelligence (AI).

The narrator took care to first distinguish between being biased and being discriminatory. We all have bias (e.g., because of our upbringing), but we should prevent discrimination. Since AI adopts our bias, we need to be more aware of ourselves so as to prevent AI from discriminating harmfully by gender, race, religion, etc.

What are some examples of applied bias? Google image search for “nurse” and you are likely to see photos of women; do the same for “programmer” and you are more likely to see men in the photos.

The narrator suggested five sources of bias. I paraphrase them as follows:

  1. Existing data are already biased (e.g., the photo example above)
  2. New training data is unbalanced (e.g., providing photos of faces largely from one main race)
  3. Data is reductionist and/or incomplete (e.g., creative writing is difficult to measure and simpler proxies like vocabulary are used instead)
  4. Positive feedback loops (e.g., past actions are repeated as future ones regardless of context)
  5. Manipulation by harmful agents (e.g., users teaching Microsoft’s Tay to tweet violence and racism)

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