Books about technology start-ups have a pattern. First, there's the grand vision of the founders, then the heroic journey of producing new worlds from all-night coding and caffeine abuse, and finally, the grand finale: immense wealth and secular sainthood. Let's call it the Jobs Narrative.

Numbers can't speak for themselves, and data sets - no matter their scale - are still objects of human design.

Books about technology start-ups have a pattern. First, there's the grand vision of the founders, then the heroic journey of producing new worlds from all-night coding and caffeine abuse, and finally, the grand finale: immense wealth and secular sainthood. Let's call it the Jobs Narrative.

Vivametrica isn't the only company vying for control of the fitness data space. There is considerable power in becoming the default standard-setter for health metrics. Any company that becomes the go-to data analysis group for brands like Fitbit and Jawbone stands to make a lot of money.

It is a failure of imagination and methodology to claim that it is necessary to experiment on millions of people without their consent in order to produce good data science.

Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define their meaning through our interpretations.

The fear isn't that big data discriminates. We already know that it does. It's that you don't know if you've been discriminated against.

We need to be vigilant about how we design and train these machine-learning systems, or we will see ingrained forms of bias built into the artificial intelligence of the future.

Like all technologies before it, artificial intelligence will reflect the values of its creators. So inclusivity matters - from who designs it to who sits on the company boards and which ethical perspectives are included.