Ethics and Data Science

Preface

We’ve seen an explosion of interest in data ethics. Why now? Well, we know: fake news, data, and all that. But concern about data ethics started well before the 2016 election. It started well before Google’s automatic photo tagging misidentified some black people as gorillas. Concern for data ethics has been growing ever since we first started talking about data science, and possibly before.

Why indeed? Because data has been integrated into every aspect of our life: the friends and business connections we’re asked to make, the shopping circulars we receive in the mail, the news we see, and the songs we’ve played. Data is collected from us at every turn: every trace of our online presence, and sometimes even traces of our physical presence. We’ve gained some advantages from data, but we’ve also seen the damage that the misuse of data has caused. And many of these concerns were highlighted in multiple reports on data and AI from the White House including the call to the United States that all training programs for data science and technology include ethics and security.

It’s been great to see people gathering to discuss ethics at events like D4G and FAT*. It’s been great to watch the lively discussions of ethical principles on the Data For Democracy Slack. And it’s been great to read the many bloggers and commentators writing about ethics.

But what we’re still missing is an understanding for how to put ethics into practice in data as well as the overall product development ...