1. Tailoring AI to the user
Excellent work so far! We will now dive into how we can design more effective AI systems with XAI in mind.
2. AI systems need to align with the end-user
Often, AI systems are used to optimize current processes. To effectively integrate AI within these processes, its important to tailor the AI system we are developing to the main stakeholder in this process.
Imagine receiving a high-tech coffee machine equipped with the latest AI to brew coffee exactly to our taste. However, it comes with a complex interface filled with confusing buttons and menus.
This scenario mirrors integrating an AI system into a process without tailoring it to its users. Just as we struggle to make a simple cup of coffee due to the machine's complexity, stakeholders can be overwhelmed by an AI system that doesn't consider their needs and expertise. It's like a skilled barista designed the system from their expertise, without tailoring it to us.
For effective integration, just like the coffee machine needing a user-friendly interface to truly benefit us, an AI system must be designed with intuitive and accessible features that align with the stakeholders' requirements, making the technology a helpful tool rather than a hurdle.
3. Scenario: the Smell Nice Shop
Take the scenario of a fictional company called the 'Smell Nice Shop'. At the shop, they want to make sure they have enough stock in their shop. Traditionally, they rely on simple past sales data to decide on stock quantities. Based on the sales of the last few weeks, the shop could decide to buy 160 bottles of shampoo. For the shop, the historical sales are an easy to understand, tangible factor, on which they base their expected required stock.
4. Scenario: the Smell Nice Shop
By leveraging an AI system capable of analyzing intricate data, including sales trends and product performances, we can achieve more accurate forecasting.
However, the complexity of this AI system might pose challenges. For instance, at the Smell Nice Shop, if the staff responsible for stock inventory lacks a technical background, they might find it difficult to grasp the AI's forecasting logic. This could potentially erode their confidence in and willingness to rely on the system.
5. Scenario: the Smell Nice Shop
At the Smell Nice Shop, an XAI-enhanced system could demystify the AI's stock level predictions by clarifying how different factors impact sales, effectively turning AI from an enigmatic 'black box' into a clear, interactive aid.
Imagine an AI system at the shop that not only forecasts shampoo sales but also illustrates the factors influencing these sales. This kind of transparency in AI's reasoning is a prime example of how XAI can enhance AI utility.
By comprehending the AI's logic, the shop owner can quickly identify inaccuracies in the AI's predictions, simplifying the process of refining the system. The ability to provide targeted feedback on the AI's decisions streamlines its improvement.
Furthermore, XAI opens up the world of AI to non-technical shop staff by unveiling the extensive data and considerations behind AI's conclusions. This insight helps them recognize overlooked variables, improving decision-making and fostering a view of AI as a valuable collaborator that enhances their own skills.
6. Let's practice!
Now that we've seen an example of how important it is to make the AI system understandable to the end-user, let's go into some exercises.