Privacy issues are likely to escalate with the increasing use of online activities in the future on social media, smartphones and search engines like Google. Data breaches, personal data leaks, and scandals are yet to erode confidence in information technology and information systems.
These incidents make data privacy an essential component of cybersecurity in a digital world. With that said, stakeholders must flip the narrative to restore the trust that the internet users look upon in information systems.
Current and future internet users should be guaranteed that advancements in technology will not threaten their privacy. They should also trust that these technological advancements will enhance privacy assurance, especially when it comes to safety and security.
Artificial Intelligence (AI) Concerns
Even though AI seems futuristic, it also has some implications. These potential repercussions come with the increased implementation of AI online. For instance, when AI starts to behave or even “think” in the same way humans do, it could threaten personal data privacy. In this regard, AI can threaten three fundamental principles of data privacy, namely:
1. Data Accuracy
One thing that makes AI unique and fascinating is algorithms to execute certain tasks. So, for AI to accurately produce outputs, algorithms need to contain many representative data sets. Otherwise, the under-representation of some groups of data sets may lead to inaccurate outcomes. Worse still, this under-representation of data sets can result in harmful decisions due to biases of the algorithms.
Usually, the algorithmic bias is created unintentionally, which can cause data inaccuracy on many occasions. For instance, studies show that smart speakers fail to understand minority or female voices. A problem such as this one arises due to the way algorithms are built, as most are based on databases containing white male voices. From this example, it would be difficult to rely on AI to handle, let’s say, emergencies such as 911 calls.
2. Data Protection
Even though large data sets produce more accurate and representative outcomes, they are also at a higher risk of being breached. Those seemingly anonymized private data may easily be de-anonymized by artificial intelligence.
Some researchers have discovered something intriguing regarding this phenomenon. They have found out that there are usually traces of minimal anonymity in all types of data, including course data sets. As a result, this minimal anonymity leads to around 95% reidentification. This statement means that individuals are at a higher risk of being easily identified and their personal data leaked to the wrong people.
Such cases often occur when data privacy considerations are ignored completely. So, in a world where AI is becoming increasingly popular, failure to consider privacy can easily lead to red flags. These cases are common when AI is utilized to analyze eligibility of federal benefits, not to mention processing taxes.
3. Data Control
Once AI starts to decipher and even define patterns and shapes, it concludes on its own. Consequently, the AI makes decisions that can help determine someone’s online experience, which is good news.
On the flip side, AI can yield false, inaccurate, or unfavorable results. When this happens, many questions are raised as to whether the decisions by AI were made fairly or not. A good example is where AI was used to help score credit risks.
So, during this process, AI can unintentionally cut some credit lines belonging to individuals with certain profiles. Sometimes these decisions may happen without notice, choice, or consent of those affected. The data responsible for causing these unfortunate events is collected without the owners’ knowledge.
Apart from that, AI is likely to infer more details about data owners. The detailed information about data owners may include their political leanings, religion, or race. This calls for data protection, especially now that AI takes over almost all online activities.
The Need for AI Privacy and Security
Often, personal data is used up against the owners’ wishes or simply against them without control. Luckily, developers can significantly reduce data privacy challenges during the development stages before production.
By doing so, internet users can realize and even appreciate the technological benefits of AI without infringing their privacy. Alternatively, private data owners can enhance privacy by including AI in their organizations’ data governance. They can also increase privacy by assigning enough resources to AI development, privacy, security, and monitoring.
Additional ways to protect data privacy in AI may include:
a. Choosing Good Data Hygiene
With this data protective precaution in place, users can worry less about data breaches simply because the data necessary to generate AI is the only one that needs to be collected. The collected data should be kept safe and maintained routinely to accomplish its intended task.
b. Considering Good Data Sets
The ultimate goal for every developer involved in this project is to use accurate, fair, or representative data sets when building AI. If need be, developers should create AI algorithms capable of auditing and ensuring that the quality of other algorithms passes the minimum thresh-hold.
c. Providing Users Control
All users have a right to know when their private data is being used. They also deserve to know if AI is being harnessed to make crucial decisions about them. Most importantly, they need to be notified about their personal data being utilized to create AI. Once they are aware of the incidents mentioned earlier, data owners should be given a choice as to whether to consent to the use of their data or not. This way, they will be better positioned to keep their data safe and monitored. For example, if your website has google reviews, you get information about your customers. You cannot use this information without their consent.
d. Minimizing Algorithmic Bias
Data sets should be broad and inclusive, especially when “teaching” AI. This is attributed to the challenges posed by an algorithmic bias towards minorities, women, and groups of individuals with vocal impairments, including the seniors. These individuals comprise a tiny portion of the entire technology workforce. Therefore, considering them can help minimize cases of algorithmic bias.
Final Thought
Increased development and widespread use of artificial intelligence (AI) is another critical element of future technological advancements. For that reason, privacy precautions must come in handy during the AI development stages to help balance technological benefits in the digital world.
