The potential ethical and social challenges present in Big Data

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­­­­­­­­­­­­­­­­­­­­­­­­The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs.

Big data is composed of larger, more complex data sets, especially from new data sources. These data sets are so voluminous that the traditional data processing software cannot manage them.

Reference: https://www.oracle.com/ie/big-data/what-is-big-data/

 

Ethical Challenges

Ethics refers to well-founded standards of right and wrong that prescribe what humans ought to do, usually in terms of rights, obligations, benefits to society, fairness, or specific virtues.

 

Informed Consent

To consent means that you give uncoerced permission for something to happen to you.

Informed consent is the most careful, respectful and ethical form of consent. It requires the data collector to give participants a reasonable and accurate understanding of how their data will be used.

Uninformed data subjects may have their data taken from their social media platforms, however Ofcom found that 65% of users accept terms and conditions without reading them, so many users may not understand the extent of data usage.

Reference: https://blog.hurree.co/the-ethics-of-big-data

 

Privacy

The ethics of privacy involve many different concepts such as liberty, autonomy, security, and in a more modern sense, data protection and data exposure.

• The concept of big data privacy:
• The condition of privacy
• The right to privacy
• The loss of privacy and invasion

 

An example of a significant data leak that caused a loss of privacy to over 200 million internet users happened in January 2021. A Chinese social media site called Sociallarks suffered a breach a hacker was able to access and scrape the database which stored:

• Names
• Phone number,.
• Email addresses,
• Locations,
• social media account login names.

Reference: https://bigdata-dialog.ch/en/the-main-ethical-legal-and-social-challenges-of-big-data/

 

Social Challenges

Big Data Divide: is the asymmetric relationship between those who collect, store, and mine large quantities of data (usually companies), and those who are the target of data collection (e.g., customers).

Recommendation: legislation should identify realistic harms that could result from Big Data divide and develop legal safeguards for those who are disadvantaged by the divide.

Reference: https://bigdata-dialog.ch/en/the-main-ethical-legal-and-social-challenges-of-big-data/

 

Discrimination and Big Data: Data bias can potentially lead to discrimination and unfairness in machine learning. However, the definition of what counts as bias is not only a technical matter, but a normative choice.

Recommendation: Anti-discrimination policies must be flexible enough to legitimate different statistical standard for different use cases

Reference: https://bigdata-dialog.ch/en/the-main-ethical-legal-and-social-challenges-of-big-data/