While Artificial Intelligence is changing industries daily, it's also raising concerns about personal privacy.
Concerns regarding unauthorized data collection and algorithmic bias have become a major spotlighted risk to privacy.
This article explores the complexities of AI and user privacy and its current legal regulations. Let's dive in!
How Does AI Affect Online Privacy?
AI systems rely on vast amounts of data to learn, adapt and perform their tasks properly.
Yet, this data is often collected without explicit consent.
Examples of data sources include online activities, social media interactions and physical movements through AI-based surveillance systems.
The Pew Research Center revealed that 72% of Americans express concerns about the personal data collected by companies.
In this context, AI-powered technologies like facial recognition systems play a significant role in this data collection ecosystem.
That raises privacy concerns about transparency, ethical frameworks and the control individuals have over their data.
Furthermore, AI algorithms are not objective or neutral— they are shaped by the data they are trained with.
If this data reflects existing societal biases, the results can perpetuate and even amplify these biases, leading to discriminatory outcomes.
The National Institute of Standards and Technology (NIST) evaluated facial recognition algorithms.
These were more inclined to falsely match images of Asian and African American faces than Caucasian faces.
This disparity was often notable, ranging from a factor of 10 to 100 times depending on the algorithm.
The MIT Media Lab also found that facial recognition systems perform worse on women with darker skin tones.
These findings underscore the urgent need to address algorithmic bias in facial recognition.
By leveraging the ethical use of AI, we can prevent discrimination and damage while protecting people!
AI Systems Data Gathering
Beyond the digital area, Artificial Intelligence tools are increasingly tapping into data from the physical world through.
Examples include sensors in public spaces, cameras and other Internet of Things (IoT) devices.
Companies often aim to improve their services by gathering data on user behavior and preferences.
However, without stringent privacy protection measures, this can lead to unauthorized access and privacy violations.
The deployment of facial recognition systems in some cities has led to debates about public safety vs individual liberties.
The Challenges of Data Repurposing
Data repurposing, where data collected for one purpose is used for another without consent, is another key issue.
This practice can expose users to privacy risks and erode trust in data handlers.
A hypothetical example could be if data data collected for a customer loyalty program.
That data could later be used to create targeted political advertisements or assess creditworthiness.
Yet, the rise of federated learning and differential privacy offers promising solutions!
Federated learning allows AI models to be trained on decentralized data without directly accessing sensitive information.
Differential privacy adds noise to datasets to protect individual identities while still enabling useful insights to be drawn.
For example, Google Gboard uses federated learning to improve word prediction on phones.
It does so by learning from users' typing patterns without transmitting what they type back to Google.
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AI Privacy Regulations
The European Union has been at the forefront of data protection.
A great instance of this is its General Data Protection Regulation (GDPR) in May 2018.
This regulatory framework sets a high standard for data protection.
Moreover, it strongly focuses on principles like individual privacy and user consent.
The EU also took a major step forward in May 2024 when it approved the AI Act.
This legislation classifies AI systems based on risk level.
High-risk applications, like critical infrastructure or law enforcement, are under stricter rules.
On the other hand, the United States still lacks an all-around federal privacy law like GDPR.
While sector-specific laws like HIPAA exist, a unified approach to data protection is missing.
California started the road with the California Consumer Privacy Act (CCPA).
Likewise, the state has also enacted the California Privacy Protection Agency (CPPA).
What are Data Protection Technologies?
Encryption techniques are, for instance, the end-to-end encryption systems used in messaging apps.
These protect data from unauthorized access so that only the sender and recipient can decrypt the information.
Likewise, privacy-enhancing technologies (PETs) like differential privacy and homomorphic encryption are gaining traction.
These technologies offer ways to perform computations on encrypted data without first decrypting it.
Another significant advance is privacy by design.
This concept integrates privacy into the initial stages of tech development.
As a result, it promotes proactive rather than reactive measures.
This can ensure that privacy is a fundamental principle guiding the development of AI.
An example is Apple's iOS system, which requires explicit consent before apps can track user activity.
Conclusion
The rise of AI holds both unprecedented opportunities and complex potential privacy risks.
At Capicua, we believe that AI can be powerful when operated ethically, with privacy as a core principle.
We're a UX-driven Product Development company with 14 years of experience.
We’re committed to developing and deploying AI solutions that respect user privacy.
We cannot wait to see that transparent and trustworthy AI can bring in the future.
Reach out to harness AI’s power by balancing between innovation and user trust!
