Ethical considerations: Big Data

Big data analytics raises a number of ethical issues, especially as companies begin monetizing their data externally for purposes different from those for which the data was initially collected.

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What is Big Data?

Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it's not the amount of data that's important. ... Big data can be analyzed for insights that lead to better decisions and strategic business moves.

Data Ethics

Data ethics is concerned with the following principles: 1. Ownership - Individuals own their own data. 2. Transaction transparency - If an individuals personal data is used, they should have transparent access to the algorithm design used to generate aggregate data sets. 3. Consent - If an individual or legal entity would like to use personal data, one needs informed and explicitly expressed consent of what personal data moves to whom, when, and for what purpose from the owner of the data. 4. Privacy - If data transactions occur all reasonable effort needs to be made to preserve privacy. 5. Currency - Individuals should be aware of financial transactions resulting from the use of their personal data and the scale of these transactions. 6. Openness - Aggregate data sets should be freely available

Further information

The scale and ease with which analytics can be conducted today completely changes the ethical framework. We can now do things that were impossible a few years ago, and existing ethical and legal frameworks cannot prescribe what we should do. While there is still no black or white, experts agree on a few principles:

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.
2. 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.
3. 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.
4. 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.
5. 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.

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

Additional Links

https://www.euroscientist.com/big-data-ethical-issues/