11 AI Data Privacy Red Flags to Ensure Client Protection

As companies race to develop increasingly sophisticated AI capabilities, the growing potential for the misuse of personal data raises red flags for legal experts.

A recent McKinsey flash survey exposes the gap between excitement and preparedness: 63% of large organizations view implementing generative AI as a high priority, yet 91% feel unprepared to handle generative AI responsibly.?

Data breaches, biased algorithms, and unfair data practices are just some of the privacy risks ahead. This article dives into these concerns and provides 11 red flags to help you protect your clients from AI's potential data privacy pitfalls.

Data breaches and unintended disclosure

FTI Technology experts predict a surge in data breaches involving Personally Identifiable Information (PII) in AI applications this year, along with more confusion regarding the extent of PII collection and storage.?

The use of PII in healthcare AI, for instance, raises concerns about data security, patient consent, and misuse of sensitive medical information. Inadvertent breaches can occur from improper sharing or storage.

Similarly, the advertising industry's online ad-tracking technologies raise legal questions about how companies collect, use, and share PII with third parties.?

These issues are particularly relevant following recent news of the FTC's $1.5 million and $7.8 million fines against GoodRx and BetterHelp for sharing patient data via advertising trackers. Class-action lawsuits against both companies quickly followed.

User consent and deceptive data collection practices

More than nine in 10 organizations recognize they need to do more to reassure their customers that their data is being used only for intended and legitimate purposes in AI. While ambiguity reigns, we can expect more lawsuits centered on user control of data in AI development.?

Plaintiffs fight for the right to see and correct the data they contribute to AI training and to opt out of data collection processes altogether. Indicators that may call for further investigation include:

• Deceptive data collection practices, such as hidden terms, manipulative web design, and misleading language?
• Policies and contracts that lack transparency about data-sharing practices
• Inadequate user consent for data collection, use, and sharing
• Harvesting more data than necessary to achieve a specific goal
• Using data for purposes other than initially consented to

Biased data and discriminatory outcomes

AI has a “black box” problem. Even its developers cannot always explain how it makes decisions. Without transparency, it is difficult to ensure AI systems are fair and unbiased.

Lawyers argue that individuals have the right to understand how AI systems make decisions that impact them, especially in areas like healthcare and employment. Biased data can lead to discriminatory outcomes in loan approvals, hiring and promotion decisions, and other life-altering recommendations.?

Then, who is liable when AI products trained on biased data lead to discriminatory or harmful outcomes? The AI system developer? The data collector? The company that used the AI tool? Government agencies, courts, and regulators are still working to develop a legal framework for AI liability, even as the technology evolves.?

11 red flags for AI Data Privacy Risks

There are several areas plaintiff lawyers can look at to evaluate whether a company proactively addresses AI data privacy challenges. To ensure accountability, consider whether an organization offers:

• Comprehensive risk management strategies that identify and mitigate potential legal risks associated with AI use and data privacy, including clear accountability for data protection measures.
• Robust data security practices and training programs that educate employees on best practices for data handling.
• Comprehensive data governance policies that define how personal data is collected, stored, used, and disposed of.
• Clear communications with users about data governance practices.

• Proper user consent management strategies for collecting and using personal information, especially for sensitive data like biometrics.?
• Individual user rights over their data, including whether they can control its use, accept compensation, or request removal.
• Data minimization techniques, such as collecting only the minimum personal data necessary for a specific purpose.?
• Data anonymization during AI training to protect user privacy.
• Safeguards against bias in AI algorithms.

• Data breach response plans that include clear notification procedures and support for affected individuals.
• Contracts with third-party vendors who access or process personal data on the company's behalf that include proper data security practices and data breach notification procedures.

By understanding these concerns, you can help protect the public from potential data privacy violations. Meanwhile, subscribe to PractiPulse?? to stay informed about the legal industry’s response to AI’s ongoing evolution and growth.?

For over 25 years, Amicus Capital Group has been helping businesses navigate the complexities of the legal landscape, including emerging trends like AI. If you have any questions or concerns about data privacy and AI, contact an experienced consultant today at 1-877-926-4287.