Dr. Mario's 2nd 🧠

What if You Were an Evil Data Scientist

2 min read

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Aren’t there checks and balances to this, like reality itself? Some, of uneven effectiveness. Decision-makers aren’t stupid and have deep domain knowledge. They have their own internal ways of triangulating results against other “known truths” of the system. You can’t just flat out lie and create data to convince them to do something bad. You need some skill and strong numbers to make a convincing case for counter-intuitive decisions. Similarly, some decision-makers will also want to be walked through an analysis and are sharp enough to spot subtle flaws. You’d have to be pretty careful about any deception employed, but I don’t think it’s impossible. Again, there’s a lot of places where if you take multiple reasonable assumptions, the cumulative effect is biased in a non-obvious way. (View Highlight)

Finding issues with data collection takes significantly more work than finding issues with existing data. Unless you know what to look for, it’s not obvious that data is being manipulated at all. Data collection issues are one of the most dangerous ones around because they’re so subtle unless you’re deliberately looking for those issues. Graphs of distributions that have missing data don’t normally scream “There’s a giant hole here!”, you really have to know where to look. (View Highlight)

I had the power to collect and analyze data, access to just about any data I wanted with some justification, could declare if a data set is “good enough to be used”, and define how to measure things. No one would really review my code or SQL queries for accuracy, because no one else had similar skills to do so. At best, people just wanted me to describe in plain terms what went into the calculations. (View Highlight)

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