"Does AI/Machine Learning Violate Personal Privacy? A Scientific Perspective"

Base Photo from K. R. Mangalam University

Multiple corporations such as healthcare, finance, and transportation have become famous for their ability to improve operational efficiency and solve complicated problems, with Machine Learning (ML) as the main factor of Artificial Intelligence (AI). According to Ray Kurzweil, “ Artificial Intelligence will reach human levels by around 2029. Follow that out further, to say, 2045, we will have multiplied the intelligence, the human biological machine intelligence of our civilization a billion-fold.” But, these improvements raise some concerns about user data privacy. This article will dive into the privacy risks connected to AI and ML systems, studying their implications for individual rights and data protection in our digital age, while also considering several solutions to said challenges.

Platforms for Machine Learning and Artificial Intelligence (AI) were created to study large sets of data to find trends and give information. Either way, personal data like names, addresses, medical records, and browsing history are often included in these sets of data. Unless suitable safeguards are put in place, the gathering and use of this sensitive data could raise serious privacy concerns. Large amounts of data, which mostly include identifying information that could endanger personal privacy, can be processed by these programs. Strong protective measures are therefore much needed to lower the risks of possible data exposure and misuse. Maintaining the security of personal data is important to uphold people's right to privacy.

To enhance their performance, artificial intelligence and machine learning algorithms usually rely on data sharing between companies. However, given the nature of the personal data that is regularly shared between different people, this dependency on other datasets presents serious privacy and data security issues. There are significant issues introduced by the possibility that data may be shared without the consent or even knowledge of people. To protect the rights of data subjects and guarantee the handling of information, this situation signifies the need for a strict overview and the application of open data governance procedures. AI and Machine Learning programs may share sensitive data with third parties, introducing individuals to possible misuse or unauthorized access.

Algorithms for Machine Learning and Artificial Intelligence use multiple tracking technologies to understand user behavior. Multiple activities, including your browsing history, location, and particular search questions, are often included in this complicated analysis. These features raise serious privacy issues although they can improve user experience and customize services. Businesses can monitor people's online behavior and use the collected personal information to build inclusive profiles that paint a picture of a person's preferences and habits. Because these tracking systems are so used, there are serious risks connected to data security and user consent. Many people may be unaware of the full extent of the monitoring or the privacy problems.

Artificial Intelligence (AI) and Machine Learning (ML) programs usually function as "black boxes," where their inner mechanisms and decision-making processes remain opaque to users. This obvious lack of transparency shows challenges for stakeholders attempting to understand the use of their data and the parties that ultimately gain access to it. Additionally, this opacity raises concerns regarding data privacy. Users are often left without insight or control over the management and dissemination of their personal information, creating an environment exposed to potential breaches of trust and confidentiality. This lack of transparency may accumulate feelings of vulnerability among individuals, as they are unable to monitor the pathways through which their data is used.

Combining AL and ML technologies creates concerns about an individual's privacy. These systems can promote data discrimination through programs that could favor particular groups over others. This can lead to increased profiling practices, where certain demographic groups are identified and targeted, often resulting in biased treatment. Furthermore, these technologies can harbor psychological manipulation, as they heighten personalized content delivery and targeted advertising strategies that influence consumer behavior in subtle yet powerful ways. The suggestions of this are far-reaching, potentially overriding the fundamental principles of fairness and privacy in our increasingly digital world.

AI and ML pose significant threats to individual privacy due to extensive data collection and pervasive tracking. These technologies can create detailed profiles of individuals without explicit consent, often lacking transparency regarding how data is used. As a result, many individuals are unaware of the extent of surveillance they face. Moreover, existing privacy protections frequently fail to keep pace with technological advancements, creating gaps in accountability. Consumers, companies, and policymakers must be aware of these challenges and adopt a cautious approach to ensure that individual privacy is respected.