Ethics and Data Science

by Mike Loukides

Moving fast and breaking things is unacceptable if we don’t think about the things we are likely to break.

the public provides the data under the assumption that we, the public, benefit from it. We also assume that data is collected and stored responsibly, and those who supply the data won’t be harmed. Essentially it’s a model of trust.

The only way to get trust back is to be trustworthy, and regaining that trust once you’ve lost it takes time.

the five Cs: consent, clarity, consistency, control (and transparency), and consequences (and harm).

Restoring trust requires a prolonged period of consistent behavior.

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.

Google Books’ ngram viewer.

All too often, we think of data products as minimal viable products (MVPs: prototypes to test whether the product has value to users). While that’s a constructive approach for developing and testing new ideas, even MVPs must address the five Cs. The same is true for well-established products.

Any member of a data team should be able to pull a virtual “andon cord,” stopping production, whenever they see an issue. The product or feature stays offline until the team has a resolution. This way, an iterative process can be developed that avoids glossing over issues.

interviewers rarely ask questions about the candidate’s ethical values.

Rather than asking a question with a right/wrong answer, we’ve found that it’s best to pose a problem that lets us see how the candidate thinks about ethical and security choices.

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