AI and Privacy Explained: How Can We Protect Our Personal Information?

Privacy and AI are not necessarily in conflict. The level of risk depends on the type of data, the purpose of use, how it is stored, and the security measures in place. However, AI's ability to identify patterns and connect scattered data can take the use of personal information beyond what users expect.

Artificial intelligence is used in movie and product recommendations, digital assistants, medical image analysis, and financial fraud detection. Many of these tools analyze data to do their job; From internet searches and purchases to sound, image and location. This raises an important question: Who is responsible for protecting our privacy when intelligent systems collect or infer more information about us?

According to Ambassador Arjan, privacy and artificial intelligence are not necessarily in conflict with each other. The level of risk depends on the type of data, purpose of use, storage method and security measures. However, AI's ability to find patterns and link scattered data can take the use of personal information beyond what the user expects.

What does privacy mean in the age of artificial intelligence?

Privacy means the right to know and control how information about us is collected, used, stored or shared with others. This information is not just name, address or phone number; Purchase records, location data, image and sound, health information and behavioral patterns can also be part of personal information.

Sometimes a few data that don't seem important on their own, together create a detailed picture of a person's habits and life. For example, search times, high-traffic locations, and purchased items may provide information about a person's interests or daily routine; Of course, the conclusions of artificial intelligence systems are not always correct and they should not be considered as definitive facts about people.

How does artificial intelligence analyze our data?

One source of data is our digital footprint: searches, clicks, time spent watching a video, online purchases, and interaction with content. This data may be used to personalize the service or display advertisements.

Another category is biometric data such as facial image, voice or movement characteristics. Face recognition can be used to unlock the phone or sort pictures; But its widespread and uncontrolled use may make it easier to track people. Biometric data is sensitive because, if exposed, it cannot be easily changed like a password.

Generative AI tools and digital assistants may also process text, files, or voice input by the user. The method of storing these entries, their retention period, and the possibility of using them to improve the service, are different in different tools. Therefore, it is better to check the privacy policy and settings of each service before using it.

Monitoring and reducing anonymity: The combination of cameras, facial recognition and location data can make it easier to track people's movements; Especially if the use of these technologies is not clear and limited to a specific purpose.

Profiling and targeting: Analyzing digital behavior may lead to useful suggestions, but misused can lead to overly targeted advertising or the inference of sensitive characteristics.

Data leakage: The more data is stored, the more extensive the consequences of data disclosure can be in the event of a security breach. Leaked data may be used for fraud, impersonation or phishing attacks.

Incorrect conclusions and discrimination: Incomplete or biased data may lead to unfair decisions in areas such as hiring, accreditation or service provision.

De-identification of anonymous data: Direct name and identifier removal is not always sufficient for anonymity. Combining a dataset with other information sometimes increases the possibility of re-identifying individuals.

Who is responsible for data protection?

Protecting privacy is not only the responsibility of users. Companies that design or implement artificial intelligence must consider data risks from the very beginning of product development. Collecting only necessary data, clearly explaining the purpose of use, limiting access, determining the retention period and having a clear plan for deleting information and handling security incidents are among the important actions of organizations.

Legislators and supervisory bodies should also clarify the rights of individuals and the responsibilities of organizations; including about the basis of data collection, informed consent, retention of sensitive information and avenues for complaints and compensation. Laws are not the same in different countries, and organizations must comply with regulations relevant to where they operate and whose data is being processed.

In the Artificial Intelligence Ethics Recommendation, UNESCO has emphasized respect for privacy and data protection in all stages of the life of intelligent systems. The National Institute of Standards and Technology (NIST) Privacy Framework also helps organizations identify and manage privacy risks.

How can users protect their privacy?

A few simple habits can reduce the risk of information disclosure:

Before using an artificial intelligence tool, review the privacy policy and settings related to the history, storage, and use of inputs.

Do not enter passwords, banking information, identification documents, medical information, or confidential work files into tools that you do not trust how to protect your data.

Enable apps to access your camera, microphone, audience, and location only when needed.

Use strong and unique passwords, multi-step authentication, and software updates.

Before publicly releasing information about your whereabouts, daily schedule, or personal situation, think about the consequences.

If a service allows you to delete history or request data deletion, use it. However, deleting data from a user account does not necessarily mean immediately deleting all backups or legal records.

Organizations can start by minimizing data; That is, only collect information that is necessary for a clear purpose. In the "privacy by design" approach, data protection is considered from the beginning of the system construction, not after the problem occurs.

Encrypting, restricting access, determining the retention period and recording access records are other security solutions. In some applications, federated learning enables model training without transferring the raw data from the device or its storage location. However, this method alone does not guarantee privacy and may require additional measures. Techniques such as differential privacy can also reduce the possibility of revealing people's information through statistical outputs under appropriate conditions.

Artificial intelligence can make services faster and more efficient, but its usefulness should not come at the cost of ignoring people's rights. Protecting privacy depends on technical, managerial and legal decisions: users must make informed decisions about the information they share, companies must manage data responsibly, and supervisory bodies must have clear and enforceable rules. The goal is not to abandon artificial intelligence; The goal is that innovation is accompanied by security, human dignity and the right of people to control their information.