This is an article about how I used machine learning to support a social cause
We, as consumers, have power. And it’s been a while since I know that. This is why I put attention in what I buy, what I read, what I recommend: who am I listening to? Who am I giving visibility to?
If you take a look at my bookshelf, you’ll find a good mix: female authors, black authors, asian authors, latin, african, muslim, and of course, some white american and european male authors as well. I find that diversifying the perspectives we have access to…
As a woman studying data analytics, I was pretty surprised and upset when I noticed something the past days: when I think about certain occupations/professions, I immediately think of men.
I went to google and typed some of these professions on “images search”, and the first pictures that appeared to me were also of men. Take a look:
So I started researching unconscious gender biases, which is defined as unintentional and automatic mental associations, stemming from traditions, norms, values, culture and/or experience. …
Background in business and marketing, also telling stories with data