When you visit a hospital, artificial intelligence (AI) models can assist doctors by analyzing medical images or predicting patient outcomes based on historical data. If you apply for a job, AI algorithms can be used to screen resumés, rank job candidates and even conduct initial interviews. When you want to watch a movie on Netflix, a recommendation algorithm predicts which movies you're likely to enjoy based on your viewing habits. Even when you are driving, predictive algorithms are at work in navigation apps like Waze and Google Maps, optimizing routes and predicting traffic patterns to ensure faster travel.
In the workplace, AI-powered tools like ChatGPT and GitHub Copilot are used to draft e-mails, write code and automate repetitive tasks, with studies suggesting that AI could automate up to 30% of worked hours by 2030.
But a common issue of these AI systems is that their inner workings are often complex to understand—not only for the general public, but also for experts! This limits how we can use AI tools in practice. To address this problem and to align with growing regulatory demands, a field of research known as "explainable AI" has emerged.
With the current move toward integration of AI into organizations and the widespread mediatization of its potential, it is easy to get confused, especially with so many terms floating around to designate AI systems, including machine learning, deep learning and large language models, to name but a few.
In simple terms, AI refers to the development of computer systems that perform tasks requiring human intelligence such as problem-solving, decision-making and language understanding. It encompasses various subfields like robotics, computer vision and natural language understanding.
One important subset of AI is machine learning, which enables computers to learn from data instead of being explicitly programmed for every task. Essentially, the machine looks at patterns in the data and uses those patterns to make predictions or decisions. For example, think about an e-mail spam filter. The system is trained with thousands of examples of both spam and non-spam e-mails. Over time, it learns patterns such as specific words, phrases or sender details that are common in spam.
Deep learning, a further subset of machine learning, uses complex neural networks with multiple layers to learn even more sophisticated patterns. Deep learning has been shown to be of exceptional value when working with image or textual data and is the core technology at the basis of various image recognition tools or large language models such as ChatGPT.
The examples above demonstrate the broad application of AI across different industries. Several of these scenarios, such as suggesting movies on Netflix, seem relatively low-risk. However, others, such as recruitment, credit scoring or medical diagnosis, can have a large impact on someone's life, making it crucial that they happen in a manner that is aligned with our ethical objectives.
Recognizing this, the European Union proposed the AI Act, which its parliament approved in March. This regulatory framework categorizes AI applications into four different risk levels: unacceptable, high, limited and minimal, depending on their potential impact on society and individuals. Each level is subject to different degrees of regulations and requirements.
Unacceptable risk AI systems, such as systems used for social scoring or predictive policing, are prohibited in the EU, as they pose significant threats to human rights.
High-risk AI systems are allowed but they are subject to the strictest regulation, as they have the potential to cause significant harm if they fail or are misused, including in settings such as law enforcement, recruitment and education.
Limited risk AI systems, such as chatbots or emotion recognition systems, carry some risk of manipulation or deceit. Here it is important that humans are informed about their interaction with the AI system.
Minimal risk AI systems include all other AI systems, such as spam filters, which can be deployed without additional restrictions.
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