How To Preserve Privacy In An AI-driven World

Data protection and privacy is a real concern in this day and age but with so many cookie policies to read and not enough time, we give up a lot more data than we expect… and that’s willingly.

.

We’re serious; request the data Google has on you and find out exactly what certain companies know.

.

But is there even a way to prevent being tracked while using the internet?

.

Not quite, but there are ways to mitigate the risks surrounding your privacy, and in reality, AI is actually crucial in protecting your data.

Can machine learning and AI be used to protect privacy in the recruitment sector?

Despite the onslaught of ‘creepy’ headlines about AI, using technology to protect privacy is possible. When AI processes data, it often picks up clues about an individual and gathers any personal information they’re likely to give. The only way to ensure your data doesn’t fall into the wrong hands is to create a policy on handling it when you’re not there.

.

People talk about how they need a chief data officer to create the right strategy for collecting, storing and working with data but the reality is that the industry needs to move away from the ‘build it quicker, not better’ technology.

.

The consequence of using AI that hasn’t been developed correctly is that it begins leaving out job positions and incorrectly matching candidates. These systems are not scalable or sustainable solutions if we’re serious about tackling discriminatory employment practices.

.

If we want to protect our privacy, at least with AI, we must ensure the data is stored in reliable, secure, and preferably encrypted servers. We also need to fight the urge to collect more data than necessary, especially within the recruitment industry.

.

Having the most expensive, data-intensive service on the planet should be seen as a business opportunity, not a threat to your customers… So how do we protect it?

How can AI be used to protect data?

When developers produce recruitment tech, the company must prepare a standard data protection policy and use data tracking and retention. In the same way, if your data is subject to GDPR, your developers should formalise your use of machine learning algorithms in a way that’s both compliant and protected.

.

Feeding consistent and trusted data into an AI system is essential to creating a Learning Machine capable of providing data privacy. Your security habits, behaviour, browser use, and even your MAC address can train a learning machine to have insights on potential security issues to keep users safe.

.

As AI understands the ins and outs of your database better than anyone else could, machine learning can quickly identify abnormal behaviour and flag them to the appropriate parties – sending out automated alerts to gauge the user’s input.

Artificial intelligence and data security

Once NLP algorithms have identified the content of a page, it can then be categorised appropriately. During categorisation, AI follows guidelines to ensure that all personal information is adequately managed, processed, monitored and used.

.

The guidelines used by artificial intelligence are laid out by the European Union’s Personal Information Protection and Electronic Documents (PIPEDA) and the Data Protection Directive, established to safeguard personal information.

.

While the guidelines are based on well-established legislative practice, AI can help prioritise data using complex algorithms that assess a user’s personal data to further mitigate risks.

.

This ability to categorise data as safe or dangerous can help to make artificial intelligence more efficient. By taking a ‘big data’ approach, companies can prioritise and filter the data they are allowed to receive from users and information deemed unnecessary, providing the necessary information for them to remain compliant with data legislation.

.

Whilst most AI applications are designed to gather and analyse large amounts of data from a user’s interaction with a webpage, we have seen that automation can specifically target and analyse specific data from large volume databases and at lightning-quick speeds.

The relationship between machine learning and AI in recruitment

While AI-driven web content management systems can provide greater efficiency for the data that is processed, data and machine learning are increasingly finding their way into web content management systems.

.

As mentioned, machine learning powers many tools and software, most notably systems where users are actively involved in making decisions. AI systems can examine a user’s input and generate their own understanding, suggesting the questions the user will want to answer to the best of their ability.

.

Machine learning in this context is also known as declarative technology, meaning that users don’t follow a set of instructions but are presented with the correct data to help them decide the best path for their final actions.

.

Here, we can see that AI systems can utilise existing information to build up a picture of the user’s actions and intent based on the system’s analysis and understanding.

Optimised Security with AI

The potential to utilise machine learning in ensuring security is endless as web content management systems can now access and process data from various sources to provide relevant data to the user.

How can AI improve the security of recruitment technology?

As with web content management systems, there are rules for how users’ private information is handled. Some regulations require all information to be stored securely, and a well-documented and secure way to access that information must be made clear.

.

When identifying a potential vulnerability, the software will protect the user’s data and contact details from that vulnerability. Therefore, the introduction of machine learning is an excellent idea as it allows the security of the personal information in such systems to be better maintained.

.

As previously mentioned, AI systems will also assist in prioritising the data and ensure that only the correct information passes through to the right person. This prioritisation means that the data is cost-effectively positioned to target the right user.

TL;DR Key Takeaways

  • Using AI to protect privacy in the recruitment industry is possible by having a policy for handling data.
  • Storing data in reliable, secure, and encrypted servers and collecting only necessary data helps protect privacy.
  • Machine learning algorithms must be formalized in a compliant and protected way to protect data privacy.
  • Feeding consistent and trusted data into an AI system is essential to creating a learning machine capable of providing data privacy.
  • AI can quickly identify abnormal behaviour and flag them to the appropriate parties to mitigate risks.
  • AI can help prioritize data using complex algorithms that assess a user’s personal data to remain compliant with data legislation.
  • Machine learning in web content management systems can provide greater efficiency for the data processed and help build a picture of the user’s actions and intent.
  • AI can improve the security of recruitment technology by identifying potential vulnerabilities, protecting personal information, and ensuring that only the correct information passes through to the right person.

.

GRID 2024 Industry Trends Report - download the report for the rundown on agencies' top priorities, challenges, and predictions for 2024.

X