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Data, Fairness, Algorithms, Consequences
By Tod Massa
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This is the subtitle of an excellent essay by Dana Boyd, Toward Accountability: Data, Fairness, Algorithms, Consequences. I've expressed before my concerns about data use and predictive analytics. This essay really nails a few points.

This is a favorite point of mine:

There is nothing about doing data analysis that is neutral. What and how data is collected, how the data is cleaned and stored, what models are constructed, and what questions are asked — all of this is political.

And a point familiar to us that is relevant with the changes Census is considering:

Census has to deal with the challenges of racial categories every ten years. It’s not easy to figure out how to do this right because it’s all wrapped up in cultural logics. Worse, it’s wrapped up in politics. The data that Census collects affects economic decisions (see: Native American communities) and shapes how politicians think about gerrymandering, not to mention the illegal practices of redlining that still go on. Census understands the political nature of their effort and works hard to develop solutions that get widespread buy-in. They don’t just think the data is neutral; they know it’s not. But the broader ecosystem isn’t as mature in its thinking.

Give the article a read when you can. It is worth your time and consideration.

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