97 Things About Ethics Everyone in Data Science Should Know

Chapter 13. Trust, Data Science, and Stephen Covey

James Taylor

CEO, Decision Management Solutions

Trust is a big deal when it comes to data science. “Black-box” algorithms, concerns about bias, and a sense that data scientists may know everything about the data but nothing about the business all undermine trust in data science models. Indeed, building data science models that can and will be trusted is regarded as a critical issue for many data science teams.

Stephen Covey once wrote a famous list about trust—the 13 behaviors of a high-trust leader. Five of these behaviors relate very specifically to leadership (talk straight, demonstrate concern, right wrongs, show loyalty, keep commitments), but the others provide a great framework for building trust in data science.

Listen First

Perhaps the most important way data science teams can build trust in their models is to begin listening to their business partners—that is, asking businesspeople how they decide and how they would like to decide, and really listening to their answers. If business partners feel heard, then they are much more likely to trust the solution the data science team creates. Working with them to develop a decision model, for instance, creates a shared understanding of the decision and a sense of being heard.

Extend Trust

Data scientists who want their models to be trusted need to extend trust to their business ...