Data Science and data analytics has raised several ethical issues over the years. This is especially so when the purpose of how the data were being collected differs from how companies are monetising these data. Furthermore, the scale and ease with which analytics can be conducted today completely has changed the ethical framework. We can now do things that were impossible a couple of years ago, and existing ethical and legal frameworks cannot prescribe what we should do. However, while there still lies a large grey area, here are 3 principles which can guide you towards a more ethical Data Science practice:

1. Private customer data and identity should remain private

As simple as it sounds, personally identifiable information obtained from your customers (or whoever) should not be exposed for use outside of its communicated purpose. These comprises of any data that could potentially identify a specific individual. Even if these data have been previously communicated and agreed to be shared amongst several entities, they should be treated with high confidentiality. Additionally, the sharing of sensitive data — medical, financial or locational — need to have restrictions on whether and how that information can be shared further.

2. Transparency

Customers who have their data collected should always have a transparent view of how their data is being used or sold. Furthermore, they should have the ability to manage the flow of their private information across massive, third-party analytical systems.

3. Big Data should not interfere with human will

Big data analytics can moderate and even determine who we are before we make up our own minds. Companies need to begin to think about the kind of predictions and inferences that should be allowed and the ones that should not.