Big data ethics serves as a branch of ethics that evaluates data practices by collecting, generating, analyzing, and distributing data. As the world expands its digital footprint, data collected has the potential to impact people and thus society.

With big data scandals left and right, users are voicing their concerns about their online privacy. Companies must adhere to a data ethics framework to maintain the trust of existing and future customers as well as business partners.

A framework exists to provide transparency, so the public knows what data you collect and why. People everywhere want to feel reassured that their data doesn’t fall into the wrong hands. Much of this data consists of Personally identifiable information (PII):

  • Full name
  • Birthdate
  • Street address
  • Phone number
  • Social security number
  • Credit card information
  • Bank account information
  • Passport number

Why is Data ethics important?

Once an organization fails to act ethically, it’s no secret that it damages the company’s brand and reputation. Similarly, after the many data scandals that occurred in the past couple of years, people lost trust in companies that manipulated customers’ PPI.

However, these scandals don’t just consist of data manipulation and sale. Housing data and keeping it safe from harm’s way also is part of big data ethics. Some of the top data breaches ever to occur had lasting effects on brand trustworthiness.

Therefore, adopting a concrete big data ethics framework is essential for the success of any large organization. Companies must act as information protectors as long as they choose to collect it.

Ethical Issues in the Big Data Industry

The bigger the data central to the business, the higher its risk of violating customer privacy and individual rights. In 2021, the responsibility to actively manage data privacy and security falls on roles within the large organization.

Privacy

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When users submit their information, it’s with the expectation that companies will keep it to themselves. Two common scenarios exist when this information is no longer private:

  1. A data breach
  2. A sale of information to a third party

Cybersecurity is a growing field. With its growth, users expect talented IT professionals to be able to protect their data. If a data breach occurs, the company fails to meet privacy expectations. Furthermore, in the 21st century, consumers expect large companies to have the means to protect data if they choose to collect it.

Lack of transparency

Many users are unaware their information is being collected. In addition to being unaware, companies go to lengths to make it very inconspicuous that they do so. Websites add cookie opt-ins on pop-ups so that the user will accept to quickly see the page.

After getting a user to submit information, some companies don’t disclose how they use a person’s data. Long lists of legal documentation are formatted in a way no user expects to read through it. It’s only after scandals or some type of media reporting do people discover the company’s data collection method is unsatisfactory.

Furthermore, once a business collects data, it can be reused for secondary purposes. This process can violate the consent of the data subject, thus not meeting the correct expectations.

Lack of governance

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Before big data, the method to collect information was simple. People either gave you a physical copy of their information or they didn’t. Companies stored the physical files under lock and key. Although someone could always potentially steal someone’s identity, criminals had difficulty doing so to masses of people at one time.

Now in a new age of information abundance, we have users unknowing submitting heaps of information. The possibilities of using that information with AI and algorithms to someone’s advantage are endless. In some countries, politicians are now creating laws to hinder certain actions. However, because big data collection is still relatively new, many laws don’t exist.

Bias and discrimination

Algorithms make assumptions about users. Depending on the assumption, they can begin to discriminate. For example, court systems started using algorithms to evaluate the criminal risk potential defendants and have used this data while sentencing. If the data encompasses a certain gender, nationality, or race then the results house bias against groupings outside of those specific groups.

Big Data Ethics Framework
and Other Ways to Resolve Ethical Issues

The government and large businesses now create a big data ethics framework to avoid ethical issues in the big data industry. While receiving initial consent, a company should develop competencies that voice how they use the data in an easily digestible manner.

Most businesses have a set of mission statements and values. Now, many will also house a big data ethics framework as well.

Data ethics in education and the social sector  

The use of data and technology strengthens biases that occur in society. Normally, this occurs in research and artificial intelligence applications. Data ethics can support the education sector in shaping policies that promote the responsible use of data and technology. Operationalizing ethical data practices require further understanding two major areas:

  • Assisting fair use of data and technology to maximize the potential to improve the public good
  • Undertaking risks that lead to negative impacts on people
man avoiding wfh burnout by establishing work like balance

Capacity-building 

Data and technology are constantly changing and enhancing, it is very hard for organizations to have the capacity to ethically follow data policies. If organizations work to build capacity, they can participate in training, creating guidance resources, and have staff be there for support.

Stakeholder engagement  

Many benefits exist when you engage with stakeholders. One consists of an increased buy-in with the use of data and the organization. Another serves as an aid to detect concerns early on, ultimately creating powerful solutions.

Developing AI toolkits  

A multitude of big technology firms is dedicated to addressing these issues with data and ethics. IBM has provided the AI Fairness 360 open-source toolkit to check unwanted bias in data sets and machine learning models. Another toolkit released was Facebook’s Fairness Flow and Google’s What-if Tool.

Work Ethically with IT Resources

Data ethics is an extremely important framework put in place to assist beneficial data and technology uses while decreasing potential harms.

At IT Resources, we care about placing candidates in ethical companies that follow a big data ethics framework. Our goal is to pair the most talented IT professionals with the best tech jobs for the future. Contact us or visit our open tech positions today to learn more about the positions we fill and how we can help you find the top talent you need.