Minimizing discrimination in machine learning

Dec 23, 2016

From Google Research, a look at how discrimination in machine learning can lead to poor results and what might be done to combat:

Here we discuss “threshold classifiers,” a part of some machine learning systems that is critical to issues of discrimination. A threshold classifier essentially makes a yes/no decision, putting things in one category or another. We look at how these classifiers work, ways they can potentially be unfair, and how you might turn an unfair classifier into a fairer one.


Visualizing the Uncertainty in Data

Data is an abstraction, and it’s impossible to encapsulate everything it represents in real life. So there is uncertainty. Here are ways to visualize the uncertainty.

Most popular porn searches, by state

We’ve seen that we can learn from what people search …

10 Best Data Visualization Projects of 2017

It was a rough year, which brought about a lot of good work. Here are my favorite data visualization projects of the year.

The Best Data Visualization Projects of 2011

I almost didn’t make a best-of list this year, but …