That's crystal clear! Let's take a step back and define open computing, so we're all on the same page. Transparent computing aims to make technology less intimidating and more intuitive for users. Users need to be able to observe and comprehend how the system works. Try to picture yourself being unable to see in a completely black environment. A lamp switch is found at last! Suddenly, when the lights are turned on, details become readily apparent. Using transparent computing, we can see the inside of computers. Why do we think it's essential for everyone to know? Stated it's the modern equivalent of the age-old maxim, "Trust, but verify." It's necessary to verify that computers perform the duties they were built for. Verification is a big problem if you can't see what's happening. The term "transparent computing" becomes helpful in this context. Users can view the system's inner workings, including any methods or processes, to verify its efficacy. How do we go about making these computer networks more transparent? Terms like "explainability" and "interpretability" are used in this context because they are unique to this field. The capacity to understand the actions of a computer system is known as "explainability." The logic and reasoning behind the system's actions can be understood. Now, do you want an automated system to make important choices for you without you knowing how it arrived at its conclusion? It's the same as trying to operate a car without knowing how it works. You could still make it, but you'd have to guess what caused the vehicle to malfunction. Thus, these systems must be explicable and comprehensible to establish responsibility and trust in technology. It's not just about making sure people pay their fair share of the bill; it's also about giving them the authority to improve the processes themselves. If you know how the systems work, you can fix flaws or enhance weak spots. Having gotten that out of the way, we can discuss "Transparency in AI." The problems of bias and unfairness in AI are growing in importance. To address these concerns, the field of transparent AI seeks to improve the explainability and interpretability of AI systems. This means that users are aware of the reasoning behind the AI's decisions and can correct any underlying biases that may be present.