Untangling algorithmic illusions from reality in big data by Alex Howard.
From the post:
Microsoft principal researcher Kate Crawford (@katecrawford) gave a strong talk at last week’s Strata Conference in Santa Clara, Calif. about the limits of big data. She pointed out potential biases in data collection, questioned who may be excluded from it, and hammered home the constant need for context in conclusions. Video of her talk is embedded below:
See Alex’s post for the video and the interview that follows.
Both are simply golden.
How important are biases in data collection?
Consider the classic example:
Are you in favor of convicted felons owning firerams?
90%+ of all surveyed say they favor gun control.
Are you in favor of gun control?
Much lower percentage saying they favor gun control.
The numbers are from memory and surveys probably forty years ago but the lesson is to watch the question being asked.
A survey that doesn’t expose its questions, how people were contacted, at what time of day, just to name a few factors, isn’t worthy of comment.
[…] a post or so ago, Untangling algorithmic illusions from reality in big data, the point was made that biases in data collection can make a significant difference in […]
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