97 Things About Ethics Everyone in Data Science Should Know

Chapter 69. Data Transparency: What You Don’t Know Can Hurt You

Janella Thomas

Data Scientist, Cox Enterprises Inc.

Transparency in data is one of the most important aspects of the ethical data science conversation. Transparency in data science primarily means effectively informing others of the data that is collected and how it will be used. Lack of transparency can result in unintended consequences for the business and can have a lasting impact on your customers. Whether you are developing analytics for internal customers or providing capabilities to external customers, data transparency has to be an integral part of the conversation.

Predictive analytics capabilities are extremely valuable and can play a strategic role in attaining the next level of an organization’s growth. When providing capabilities that will only be used internally, one of your primary responsibilities is to inform stakeholders of what data you’re using and how it will be used. However, informing others of the data collection and intended usage is not enough. It is important to go a step further and analyze potential outcomes of the tools we provide. There are applications of predictive analytics that require ethical analysis.

For example, Target Corporation used a predictive model to score the likelihood of pregnancy for marketing purposes. The consequences of Target’s predictive model resulted in a father ...