It was a typical day in the office of a privacy practitioner, Jane, when she received an alert notifying her of a data breach in one of the company's AI-powered applications. Jane was not surprised, as she knew that AI technology had been evolving at an incredible pace, making it more challenging for privacy practitioners to keep up with the associated risks.
As Jane delved deeper into the data breach incident, she discovered that the AI application had been using a dataset that contained sensitive customer information, including names, addresses, and credit card numbers. The dataset was not properly secured, and hence, was vulnerable to external attacks.
Jane realized that AI technology was evolving rapidly, and privacy practitioners must adapt and keep pace with its developments to minimize the risks of data breaches and other privacy violations.
Privacy Risks in AI
- Google's DeepMind was found to be in violation of privacy laws for wrongly collecting medical data from NHS patients without their consent in 2017. The UK's Information Commissioner's Office (ICO) fined DeepMind £2.6m.
- Facebook had to pay a $5bn fine in 2019 to settle a privacy violation case with the Federal Trade Commission (FTC), where the social media giant was alleged to have mishandled user data during the Cambridge Analytica scandal.
- Amazon's AI-powered recruitment tool was found to be biased against women in 2018, as it used data from male-dominated resumes to screen candidates, leading to discriminatory hiring practices.
Addressing Privacy Concerns in AI
1. Understanding the Data Used by AI Systems
AI systems rely on vast amounts of data to learn and improve their performance. Privacy practitioners must understand what data is being collected, how it is being used, and who has access to it. Data collection must be transparent, and privacy policies must be clearly communicated to users.
2. Ensuring Data Security and Protection
The sensitive nature of the data used by AI systems makes them vulnerable to external threats. Privacy practitioners must ensure that data is encrypted, secured, and backed up. They must also develop effective incident response plans in case of a data breach.
3. Mitigating Bias in AI Systems
AI systems are only as good as the data they use. Privacy practitioners must ensure that AI systems are not biased and do not discriminate against any group based on race, gender, or any other protected characteristic. They must also conduct regular audits to identify and mitigate any bias in AI systems.
Practical Tips for Privacy Practitioners
- Engage with AI experts to understand the technology and its associated privacy risks.
- Develop a data governance framework that includes policies, procedures, and controls for the use of AI systems.
- Conduct privacy impact assessments to identify and mitigate any privacy risks associated with AI systems.
- Partner with legal and regulatory experts to ensure compliance with data protection laws and regulations.
Conclusion
AI technology presents a unique set of challenges for privacy practitioners, who must keep pace with its rapid evolution to minimize the risks of data breaches, privacy violations, and discriminatory practices. By understanding the data used by AI systems, ensuring data security and protection, and mitigating bias in AI systems, privacy practitioners can help organizations harness the potential of AI technology while safeguarding user privacy.
- Reference URLs:
- https://ico.org.uk/about-the-ico/news-and-events/news-and-blogs/2017/07/ico-announces-1-2million-fine-for-mps-following-serious-data-breach/
- https://www.ftc.gov/news-events/press-releases/2019/07/ftc-imposes-5-billion-penalty-sweeping-new-privacy-restrictions
- https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G
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Curated by Team Akash.Mittal.Blog
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