5 Principles for Big Data Ethics

https://towardsdatascience.com/5-principles-for-big-data-ethics-b5df1d105cd3

  1. Private customer data and identity should remain private: Privacy does not mean secrecy, as private data might need to be audited based on legal requirements, but that private data obtained from a person with their consent should not be exposed for use by other businesses or individuals with any traces to their identity.

  1. Shared private information should be treated confidentially: Third party companies share sensitive data — medical, financial or locational — and need to have restrictions on whether and how that information can be shared further.

  2. Customers should have a transparent view of how our data is being used or sold, and 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.

  4. Big data should not institutionalize unfair biases like racism or sexism. Machine learning algorithms can absorb unconscious biases in a population and amplify them via training samples.