Are Data Scientists Actually Surveillance Scientists? - Part 1

There was of course no way of knowing whether you were being watched at any given moment. How often, or on what system, the Thought Police plugged in on any individual wire was guesswork. It was even conceivable that they watched everybody all the time. But at any rate they could plug in your wire whenever they wanted to. You had to live—did live, from habit that became instinct—in the assumption that every sound you made was overheard, and, except in darkness, every movement scrutinized. -1984, George Orwell

Last summer I had a conversation with an acquaintance who had recently visited China. There was discussion about China's Social Credit System (SCS) and its impact on people's daily lives. The Social Credit System is a system that assigns scores to citizens based on their reputation, and that score can impact someone's ability to be outside in the evening, their eligibility to book a travel ticket or their suitability for a loan. It's similar to a Credit Score that North Americans are more familiar with but more encompassing as the SCS takes non-financial data into account. My acquaintance said that the initial feedback was positive - her friends and family felt safer walking the streets at night knowing that people deemed dangerous wouldn't be allowed outside. 

How studying data science lets me design better customer solutions

If data is the new oil, then data science is the new refinery.

I was recently asked whether studying Data Science has helped me in my day-to-day job. My response was yes, but not in an obvious way - it's resulted in better designed customer solutions by improving my empathy.

Let me take a step back. For the past few years, I've been leading Software-as-a-Service (SaaS) platform integrations for enterprise clients. I often describe the work as similar to being a clothing tailor. If a software consultancy is a bespoke tailor that customizes every detail at a premium price; than, a SaaS platform is a made-to-measure tailor who cuts from an existing pattern at an economical price. Over time, I've learned how to measure and cut software for customers of all shapes, sizes, and sophistication.

Why soft skills will win in the age of machine learning

Back in college, I had a summer job completing research for a clinical health professor. She was a leading expert in diagnosing and treating open human wounds. My job was to survey other experts, get them to examine photos of open wounds, and then recommend a treatment.

A few months ago, I discovered a smartphone app which replaces this work.* You take a photo of an open wound and upload it to the cloud. I suspect that the photo is run through an image recognition model, called a Convolutional Neural Network (CNN), that identifies specific features of the wound for treatment. Current machine learning is very good at completing narrowly defined tasks, such as analyzing a specific type of medical image, because they have millions of previous examples to train from. It is not good at handling non-standard cases. 

How Data Scientists Are Controlling Your Life

My daily experience with recommendation systems are seamless. They recommend what to read on Apple News, listen on Spotify, eat on Uber Eats, purchase on Amazon, watch on Netflix. These software programs take millions of data points, clean and segment the data, weigh different variables, and output recommendations that ensure we stay engaged with the platform for the next selection. As much as we want to believe that machines make all these decisions, data scientists are the ones that are deciding the inputs for these models. Ultimately, these choices introduce bias. 

What if I'm missing out on an incredible book or song because the inputs don't capture interests of mine that I didn't even know existed?