Privacy Considerations of AI

Last time, we continued our “nuts and bolts” series of artificial intelligence (AI) for legal professionals with a look at how bias can impact how AI algorithms work and how their output is analyzed.

AI algorithms are driven by data – lots and lots of data. One of the challenges associated with so much data driving AI algorithms is that at least some of that data may be sensitive individual personal data. In fact, the revenue model of numerous companies today is built on the ability to use personal data to drive recommendations on everything from shows to watch on a streaming platform like Netflix, to songs to listen to on Pandora, and items to purchase on Amazon. Social media platforms like Facebook, Instagram, and X (formerly Twitter) use AI algorithms to determine what content appears in the feed of its users. Facial recognition systems are being used with increasing regularity – often, without us even realizing it. The list goes on.

The handling of all that personal data has not only led to considerable concern from a data privacy perspective, but it has been a major catalyst for new comprehensive data privacy laws, such as Europe’s General Data Protection Regulation (GDPR). Article 22 of the GDPR addresses automated processing, stating: “The data subject shall have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her.”

Conversely, there are also AI mechanisms that help protect sensitive data – ranging from personally identifiable information (PII) detection and anonymization to consent management – so the AI impact on data privacy can be positive as well. In this post, we’ll discuss the privacy considerations – negative and positive – of AI.

Privacy Concerns Associated with AI

There are several privacy concerns associated with the use of AI. They include:

• Data Collection and Usage: As noted above, AI algorithms generally require large amounts of data to train and operate effectively. This data often includes sensitive personal information, which could be misused, mishandled, or accessed by unauthorized parties.
• Data Profiling and Decision Making: AI can be used to create detailed profiles of individuals based on their online behavior, purchasing history, and other digital footprints. These profiles can then be used to make decisions that affect individuals’ lives, such as credit scoring, hiring, or insurance pricing, sometimes without their knowledge or consent. There have been over € 4.4 billion in fines under GDPR for practices like that since it went into effect in May 2018, most of that (nearly € 3 billion) in the last 2 years.
• Consent and Choice: Speaking of consent, individuals are often not aware of when and how their data is being used for AI purposes. This raises questions about the validity of consent and the degree of choice individuals have regarding their personal information.
• Inference of Sensitive Information: AI can be used to infer sensitive information about individuals from their data and activities. For example, purchasing patterns or social media activity could reveal health status, political affiliations, or other private details.
• Surveillance and Monitoring: AI technologies, such as facial recognition and predictive analytics discussed in our last post, can be used for extensive surveillance. This raises concerns about constant monitoring and the potential erosion of privacy in public spaces.
• Data Vulnerability: The indefinite retention of personal data for AI purposes increased the vulnerability of that data, as the longer data is held, the more likely it is to be improperly accessed by hackers or other unauthorized individuals. Potential Benefits of AI for Data Privacy

However, AI isn’t all bad when it comes to data privacy – the use of AI mechanisms can strengthen data protection. Potential benefits of AI for data privacy include:

• PII Data Detection and Anonymization: AI can identify personal and sensitive data in large datasets. It can anonymize data by removing or redacting PII, ensuring that individual privacy is maintained while the data is still useful for analysis.
• Enhanced Encryption Techniques: AI can optimize encryption methods, making it more difficult for unauthorized parties to access sensitive data. AI algorithms can also help in the development of new encryption techniques that allow data to continue to be accessible while still encrypted.
• Intrusion Detection and Prevention: AI systems can monitor network traffic and user behavior to detect unusual patterns that may indicate a security breach. By learning from past incidents, these systems can identify and respond to threats in real time to protect sensitive data.
• Data Access Control: AI can help in managing and controlling access to sensitive data. It can analyze access patterns and flag or block unusual access requests, reducing the risk of data breaches.
• Phishing Detection and Prevention: AI algorithms can be trained to detect phishing attempts more effectively than traditional methods. They can analyze email patterns, website authenticity, and other indicators to identify and block phishing attacks.
• Automated Compliance Monitoring: AI can assist organizations in complying with various data protection regulations (like GDPR). It can automate the process of monitoring and auditing data usage to ensure compliance with legal and ethical standards.
• Consent Management: AI can manage and track user consents and preferences regarding data usage, ensuring that data is used in compliance with individual choices and legal requirements.
• Biometric Security Systems: AI-powered biometric systems (like facial recognition, fingerprint scanning) can provide robust security measures for data access, ensuring that only authorized individuals can access sensitive information.

Conclusion

When it comes to AI and the data it uses, privacy will always be a consideration. Data privacy laws have emerged to address concerns over the use of sensitive and personal data, but the battle between regulators looking to protect individual rights and companies looking to maximize profits will continue for some time to come. If you’re a company with a large collection of data to protect, it’s important to: 1) maximize the protection of that data, and 2) ensure compliance with key regulations like GDPR.

However, AI does provide several benefits in helping to protect sensitive and personal data, especially from hackers and other unauthorized parties. That’s a positive consideration when it comes to privacy concerns and AI, and you want to take advantage of AI mechanisms to secure that data.

Next time, we’ll discuss transparency and interpretability considerations associated with AI and the “black box” associated with many implementations of AI technology.

For more regarding Cimplifi security, privacy, and compliance solutions, click here.

In case you missed the previous blogs in this series, you can catch up here:

The “Nuts and Bolts” of Artificial Intelligence for Legal Professionals

The “Nuts and Bolts” of AI: Defining AI

The “Nuts and Bolts” of AI: Types of Bias in AI

The “Nuts and Bolts” of AI: Privacy Considerations

The “Nuts and Bolts” of AI: Transparency, Explainability, and Interpretability, of AI

The “Nuts and Bolts” of AI: ABA Guidance on the Use of AI

The “Nuts and Bolts” of AI: The Current State of AI Regulations

The “Nuts and Bolts” of AI: Current Proven AI Legal Use Cases

The “Nuts and Bolts” of AI: Emerging Use Cases and the Future of AI for Legal

We invite you to stay informed and join the conversation about AI. If you have questions, insights, or thoughts to share, please don’t hesitate to reach out.