Ethics and Data Science

by Mike Loukides

We need to understand how to build the software systems that implement fairness. That’s what we mean by doing good data science.

A better world won’t come about simply because we use data; data has its dark underside.

A checklist isn’t something you read once at some initiation ceremony; a checklist is something you work through with every procedure.

Unfortunately, the process of consent is often used to obfuscate the details and implications of what users may be agreeing to. And once data has escaped, there is no recourse. You can’t take it back.

Collecting data that may seem innocuous and combining it with other data sets has real-world implications. Combining data sets frequently gives results that are much more powerful and dangerous than anything you might get from either data set on its own.

“Growth hacking” focuses on getting people to sign up for services through viral mechanisms. We’ve seen few product teams that try to develop a user experience that balances immediate experience with long-term values.

Data-driven organizations need a similar model that allows people to escalate issues without the fear of reprisal. An escalation process could be implemented in several forms.

Going fast doesn’t mean breaking things. It is possible to build quickly and responsibly.