GDPR: big data and AI
1. GDPR: big data and AI
Welcome back! Well done on reaching the final part of the course.2. Overview
We'll take a broader look at GDPR- its influence on technologies like AI, the risks and opportunities related to GDPR compliance, data protection, and ethics, and the relationship of GDPR with global and upcoming regulations. Let's start by asking ourselves, what is AI?3. What is AI?
Everyone talks about Artificial Intelligence, but no one can agree on one definitive definition of AI. According to the European Commission's high-level expert group on AI, Artificial intelligence (AI) systems are software and hardware systems designed by humans to achieve a specific goal. They act in their physical or digital environments to achieve their goals through data acquisition, processing, and interpreting the data to decide on the best actions to take to achieve the goal. Okay, that sounds very high-level and abstract; we'll see some AI terms you maybe be more familiar with and use cases shortly. But I want you to remember that AI requires data, and often a lot of it.4. AI approaches
According to the expert group, the common AI approaches can be divided into three macro areas. Machine Learning techniques like deep learning with artificial neural networks can process massive amounts of data. Machine reasoning includes planning, scheduling, information retrieval, search, and optimization. And robotics covers cyber-physical systems that require control, perception through sensors and actuators, and the integration of all the required hardware and software. While this course is not about AI, it is essential to have at least a basic overview to understand data's role in AI and the connection to GDPR.5. How is AI used?
You will often hear from AI practitioners that 90% of the data in this world was created just in the past five years. AI has the potential to utilize this data to propel innovation and competitive advantage for companies. Some of the typical AI applications or use cases you may not even realize have AI under the hood are: product or movie recommendations from your online shopping portal based on your shopping or view history. Automated systems for screening CVs for job positions and virtual assistants like Alexa or Siri. Many use cases that use substantial amounts of data are often based on Machine Learning, one of the three AI approaches we mentioned earlier.6. ML: AI systems that learn from data
Machine learning systems are composed of algorithms, the data they learn from, and the infrastructure they need to train and function, and humans design them for a specific purpose. The rise of big data and the availability of computational resources have been instrumental in the advances and performance of machine learning, making them readily available. Machine learning is potent for enabling large-scale data processing and profiling individuals into desired groupings. Some machine learning systems are so complex that it is hard to understand how they make decisions, even by the person who designed them. Now let's see how GDPR fits in all this.7. Where does GDPR fit in?
If you plan to implement AI projects in your organization, you must consider if it is GDPR compliant for several reasons. First and foremost, AI systems, especially those based on Machine Learning, process large quantities of data, often containing personal data that falls under GDPR. Large-scale processing may raise conflicts with GDPR data minimization and accountability principles since these datasets may come from multiple sources. It also poses unique security and disclosure risks, given the amount of personal data processed. In most cases, AI systems would need a data protection impact assessment or DPIA to evaluate and mitigate such risks. The most important principle of GDPR that has the biggest impact on AI is that of lawfulness, fairness, and discrimination, especially when it comes to automated decision making and profiling. Let's find out more on this, in the next video!8. Let's practice!
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