1. There’s a conference coming up in Canada on “Fairness, Accountability, and Transparency in Machine Learning”, a topic I wrote a little about for the Listener
Questions to the machine learning community include:
- How can we achieve high classification accuracy while eliminating discriminatory biases? What are meaningful formal fairness properties?
- How can we design expressive yet easily interpretable classifiers?
- Can we ensure that a classifier remains accurate even if the statistical signal it relies on is exposed to public scrutiny?
- Are there practical methods to test existing classifiers for compliance with a policy?
Democrats may not be wrong. The polls could very well be biased against their candidates. The problem is that the polls are just about as likely to be biased against Republicans, in which case the GOP could win more seats than expected.
This sort of slowly varying bias is probably one of the reasons the NZ election polls weren’t very good: not only did they have more variability than you’d expect given the sample sizes, but averaging didn’t cancel out much of the error.
3. Yesterday was Spreadsheet Day. Flee in terror! (via @kara_woo)
4. An informative visualisation of what the world eats, over time. (via Harkanwal Singh)