1. Why Explainable AI matters
Welcome to this course on Explainable Artificial Intelligence. My name is Folkert, and in my work I aim to bridge the gap between complex algorithms and practical applications. This is a core component of Explainable AI.
We will first dive into why Explainable AI is crucial.
2. An AI system
The abilities of AI systems can be categorized into two main functionalities. On the one hand, an AI system is able to predict. It can analyze data, identify patterns, and make forecasts. Predicting market trends, consumer behavior, health risks, and weather patterns.
AI systems can also generate content. ChatGPT and other generative AI implementations can create text, code, images, and even music.
3. Explainable AI (XAI)
Explainable AI, also known as XAI, can be seen as a translator between AI systems and human understanding. Just like a translator helps bridge language barriers, XAI bridges the gap between advanced AI technologies and our comprehension. Throughout this course, we will look into ways in which XAI achieves this.
Key concepts of XAI revolve around transparency, interpretability, accountability, and trust in AI. Let's start with transparency.
4. Transparency in AI
Imagine driving a car with opaque windows; navigating becomes impossible. This is similar to using AI without transparency. The essence of transparency in AI lies in the ability to trace and comprehend the decision-making process of AI systems, ensuring not only the efficacy of these decisions but also their justifiability.
5. Interpretability and accountability
Achieving transparency can be done by developing interpretable and accountable AI systems.
Interpretability in AI is the extent to which a human can understand the cause of a decision made by an AI system.
It's not enough that the AI system reaches a perfect conclusion, it must explain its logic in a way that humans can understand and trust. It must do so in a way that is interpretable. This is especially important in fields like healthcare.
If an AI system identifies a possible diagnosis, it's essential for healthcare professions to understand the reasoning to validate and act on it confidently. This clear understanding fosters accountability, ensuring that AI acts in accordance with ethical and societal norms.
Accountability in AI refers to assigning responsibility for the actions and decisions made by AI systems.
6. Building trust in AI systems
Consider the transition from manually operated to automatic elevators. Suddenly, a voice announces your arrival at the desired floor. This shift mirrors our hesitance to trust computers taking over tasks traditionally done by humans, underscoring the challenges in adapting to technological advancements.
Trust in AI is cultivated when users understand and can predict how it behaves. In AI, this means creating systems that not only make decisions but also explain them in a relatable manner. This clarity transforms AI from a mysterious black box, into a reliable, transparent tool, enhancing user confidence and providing a good relationship between humans and technology.
7. Why does XAI matter?
To summarize, XAI is crucial because it ensures that AI decisions are not only effective but also justifiable, making the decision-making process clear and understandable. By fostering accountability, XAI guarantees that AI systems operate in alignment with ethical and societal standards, promoting trust and fairness. Furthermore, XAI transforms AI into a transparent and reliable tool, boosting user confidence and nurturing a positive relationship between humans and technology.
8. Let's practice!
Now let's test our knowledge and remember why XAI matters with some exercises!