Privacy PowerUp Series #8
People see and hear so much about artificial intelligence, but do people really understand AI and machine learning? Furthermore, how do privacy professionals fit into the world of AI?
Understanding AI and machine learning
What is artificial intelligence (AI)?
When you hear about AI, images of sentient robots from science fiction are often conjured. However, in reality, AI refers to machines, commonly computer systems, that can simulate human functions and processes such as learning and self-improvement. These systems are built, programmed, and maintained by humans; they do not think or act with an independent consciousness.
The role of machine learning
AI is an umbrella term that includes various technologies and learning approaches, one of which is machine learning.
Machine learning is a data-driven type of AI that analyzes data to teach machines how to perform specific tasks and provide accurate results by identifying patterns. Over time, machine learning algorithms improve as they are exposed to more data, enabling the AI model to learn and enhance its performance.
Automated processing vs. AI
It’s crucial to distinguish between automated processing and AI. Automated processing focuses on task execution based on predefined programming by humans. AI, on the other hand, emphasizes decision-making that replicates cognitive processes, learning from new data, and improving its outputs over time.
Key privacy challenges in AI
Data minimization
One of the most significant privacy challenges with AI technologies is data minimization. AI systems often require vast amounts of data to improve their learning. Collecting this data, especially without the knowledge and consent of individuals, can pose severe privacy risks. Businesses must exercise due diligence in ensuring compliance with data minimization principles.
Transparency and explainability
Transparency issues arise when there is a lack of understanding about the data sources feeding into an AI model and how the AI system works.
Individuals should always be informed when they are interacting with AI. Updating privacy policies is not enough; businesses must communicate these updates effectively to meet transparency requirements.
Data security
Storing large amounts of data for AI systems increases the potential for data breaches and improper access. Ensuring robust security measures is vital to protect this data from unauthorized access and leaks.
Discrimination and bias
AI systems can perpetuate discrimination and bias, especially when used for profiling and automated decision-making. Biases in the AI’s algorithm, often stemming from the biases of human developers, can lead to unfair treatment of individuals or groups.
Is your AI governance program ready for rapidly evolving AI technologies? Take a brief quiz to find out!
Addressing privacy challenges in AI
Understand your AI
Know what type of AI you are using, how it works, and how your business employs it, particularly if personal information is involved in training or decision-making processes.
Stay updated with laws
Stay current with existing and emerging laws to ensure your AI usage and associated data processing comply with privacy and security requirements.
Assess high-risk processing
Determine if your AI processing would be considered high-risk. Conduct a data protection impact assessment (DPIA) where required, especially when using new technology to process personal data.
Revisit privacy management
Incorporate AI into your existing privacy policies and procedures. Update privacy notices, processes for handling individual rights requests, and data retention and security policies to address AI technologies appropriately.
Ensure transparency and explainability
Make AI system operations, algorithms, and decision-making processes visible and understandable to users and stakeholders. Provide clear explanations for AI decisions to enable users to understand the outcomes.
Implement human oversight
Ensure appropriate human oversight and intervention mechanisms are in place. Allow individuals to question or challenge AI decisions and ensure a human operator can review and take over the decision-making process when necessary.
Strengthen data governance
Effective data governance is crucial in the AI context. Ensure the use of accurate and quality data, promote transparency, accountability, and ethical considerations in AI development and deployment.
Practice privacy by design
Integrate privacy safeguards into every operational phase as you build automated systems and AI models. Ensure these safeguards are not just applied retroactively due to compliance requirements or data breaches but are part of the design process from the start.
Learn more about integrating privacy by design principles into the software development life cycle.
Guiding AI with privacy: Essential strategies to ensure compliance and trust
Privacy professionals play a crucial role in managing privacy and emerging technologies. By understanding the key terms, recognizing privacy challenges, and taking proactive steps to address these challenges, you can ensure that your use of AI aligns with privacy and security requirements.
Remember, transparency, explainability, and human oversight are fundamental principles that should guide your AI compliance plan. By incorporating these elements into your privacy management program, you can build trust with your users and stakeholders while leveraging the benefits of AI.
How well are you managing your AI risk?
Understand your AI compliance requirements, accelerate your governance program, and demonstrate responsible AI use.
Improve your AI risk management todayContinue mastering the privacy essentials by reviewing all the resources in the Privacy PowerUp series.
Emerging Technologies in Privacy: AI and Machine Learning Infographic
Understand the privacy challenges associated with AI and ML.
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PowerUp Your Privacy
Watch all ten videos in the Privacy PowerUp series – designed to help professionals master the privacy essentials.
Watch nowRead the next article in this series: #9 Privacy Program Management: Buy-in, Governance, and Hierarchy.
Read more from the Privacy PowerUp Series:
- Getting Started in Privacy
- Data Collection, Minimization, Retention, Deletion, and Necessity
- Data Inventories, Mapping, and Records of Process
- Understanding Data Subject Rights (Individual Rights) and Their Importance)
- The Foundation of Privacy Contracting
- Choice and Consent: Key Strategies for Data Privacy
- Managing the Complexities of International Data Transfers and Onward Transfers
- Emerging Technologies in Privacy: AI, Machine Learning, and Others
- Privacy Program Management: Buy-in, Governance, and Hierarchy
- Managing Privacy Across the Organization
