Data is an essential resource today. All businesses should be using it or working to have data-centric operations. This is in line with growth and competitive advantage for all companies.
However, there is a limit to what data can be collected, stored, and processed. With the need for data to ensure competitive advantage and growth comes the need for privacy regulations.
Data protection laws lay down the rules that must be followed to protect personally identifiable information, personal data, as it relates to processing and movement. In this, movement implies data sharing. Data protection regulations such as the GDPR are essential for everyone, as they enhance people’s privacy. However, they are a limiting factor in data science and AI. For data science and machine learning to prosper, data is integral. Not just any data but data in ample supply, varieties, and diversities.
Therefore, the question that arises is: how do data professionals navigate the minefield of privacy regulations without compromising or breaching the law?
Various actions can be taken to ensure that the specified regulations are followed and that the resulting applications and platforms do not breach the rules. Some of these methods entail how data is stored, processed and the control provided by the owner.
Data catalog
Data collectors bring about one issue is not informing their customers about what data they collect. As a result, the data owner is unaware of how much information has been collected. Additionally, the use of data is not communicated to customers.
A good approach to address this is to communicate to the customers what data is being collected and what types are being stored. To process, say, most transactions online, such data as the names and credit card numbers are necessary.
Similarly, location data is essential in such scenarios. However, not all data is needed for the transaction to be completed. Collecting only the necessary data and communicating it in a catalog of the content stored about the customer is essential. This will help address the issue of data collection regulations being breached.
Information security
One provision of the famous data protection regulations, GDPR, requires the communication of data breaches to customers. However, it is far more important to prevent unauthorized personnel from accessing this data. Today this investment in data security is important. Some governments are still against the use of shared cloud data storage. This is due to the implications they perceive from storing data in foreign countries.
As a result, investing in information security and information storage to ensure the infrastructure and provisioning meet customer needs is important. Encrypting the data and storing it with anonymized user names and identification is also a way to avoid losing confidentiality when unauthorized persons access the data.
Data processing.
The purpose of storing data is to use it to gain a market advantage, make decisions, and strategize the way forward. To achieve this, income data scientists and machine learning engineers. The purpose is to use the data to generate insights and models to help the organization make good decisions and plans for its operations. However, how the information is processed and what information is processed can be unethical. When facial recognition was first implemented, there were issues with certain unedifying races as faces.
This resulted in significant concern about the propagation of racism by these models. However, this issue would not have been corrected without intensive data modeling and training using vast amounts of data. Data processing is important, and to avoid data regulation issues, the model can store precise data about an individual. In this case, I suggest using the processed results rather than the raw data. Storing processes' data would improve their anonymity, as the reverse would seemingly be impossible.
Data quality and control
Organizations need to implement data quality mechanisms that support data accuracy control and allow their users to correct, request, and delete their data whenever they want. As has been seen on most social media platforms recently, users can update their information, request their data, and delete their accounts and data altogether.
This will ensure that the user is comfortable and that their data is in their hands. Additionally, the companies will be able to comply with the data regulations.
Preventing bias in the model
One issue with machine learning models is that some will give biased results. In models for predicting criminal behavior, one problem to note is that there will be bias based on the available data. Imbalanced data can lead to biased models.
A data scientist would ensure that the data is well-balanced before using it to train the model. Another engineering mechanism should also ensure that the resulting models address such issues. This will prevent biased and wrongful arrests and build user confidence in the models and the tools derived from them.
While data science and the resulting benefits are important to businesses and people, it is crucial to ensure that, first, the regulations governing the use of this data are met, and that the resulting platform that uses data enables ethical decision-making and upholds the moral norm of impartiality.