Ethics and Big Data

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There can be no argument that Big Data has become a saturated topic of discussion among software developers and data managers. And there lurks the danger of them starting to treat it as just a massive number, which might be true for them but not for the millions of humans who have generated or own that data. Here comes the need of Big Data ethics, which are required to ensure that those millions of humans are not cheated into parting with confidential information unknowingly.

What can be done?

Here are some views:

  1. Transparent practices: When users are informed how information about them will be used or ‘sold’ in real time, it will take care of the problems of unauthorized tracking and hidden files. Google is one company that has put this theory into practice and allows users to see what it knows about them and the inferences that have been drawn from it. You can visit to know the details. Taking it a step further, companies need to allow privacy settings – simple privacy settings – that make it easier for a user to control the amount and kind of data they want to go public or remain private. However, if the privacy options are difficult to navigate or figure out, the purpose of having them in the first place is defeated to quite an extent.
  1. In-built privacy: Some argue that organizations need to incorporate privacy settings in everything they do, which means that they need to set customer privacy as a guiding principle right from the beginning. When software is developed embracing this principle and the users are kept in the know, it becomes a valuable asset for the company to gain customer trust.
  1. Transparent exchange of value: Targeted recommendations on the web are a feature everyone likes because it makes life simpler for both the customer and the service provider. However, if the recommendations are provided on the basis of an openly-acknowledged database, it creates an environment of trust as well as encouragement for future usage of user data, or the accumulation of Big Data.
  1. Restrictions on predictions: What happens when an algorithm starts to define personalities? A person might get determined by technology even before they begin to determine themselves. Institutional surveillance carried out by big data predictions can turn this into reality. And therefore, there’s the extremely important need to think about the inferences that should be allowed, and should be restricted.

The proposals mentioned here are by no means exhaustive. However, these can be an outline on which companies can develop their data handling policies and mitigate risks related to privacy of Big Data. If we want a digital world that we would like to be fair to ourselves, we need to take care that we are fair with the data of others. Big Data is much more than just databases and algorithms that identify patterns, it is a tool for obtaining money and power. Therefore, like all other means of collecting riches, this also has to pass through tests of fairness and transparency.

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