XAI in action

1. XAI in action

Now we'll delve into how XAI is revolutionizing various sectors, showcasing real-world instances where transparency and interpretability enhance decision-making. We'll examine the dynamic application of XAI, shedding light on the tangible benefits and innovative solutions it brings to diverse industry challenges.

2. Computer vision: detecting skin cancer lesions

A compelling case study is the application of computer vision in diagnosing skin cancer. Traditionally, dermatologists visually examine skin lesions and may perform biopsies to confirm cancer. Now, AI models, trained on thousands of images of skin conditions, assist in identifying malignant lesions with high accuracy. However, the complexity of these models often made their decisions seem like a "black box," raising concerns about reliability and trust.

3. Computer vision: detecting skin cancer lesions

XAI comes into play by providing insights into how these models arrive at their conclusions. For example, an AI system might highlight areas of a lesion image that were most indicative of malignancy, offering dermatologists a comprehensible rationale behind the AI's diagnosis. This transparency not only builds trust among healthcare providers but also enriches their understanding, enabling more informed decision-making. Additionally, the AI system can provide a confidence score, which is an indicator of how confident the AI system is in detecting the malignancy. In this way, the dermatologist understands the decision-making, and can decide whether or not to intervene with the classification.

4. Natural language processing: sentiment analysis

Natural Language Processing, also called NLP, is a known component of Artificial Intelligence. Instead of analyzing images like we saw in the example of computer vision, we are know focusing in on the analysis of words. We can use sentiment analysis to analyze the sentiment of customer reviews for a shop. If the shop has a large amount of reviews, automatically analyzing the amount of positive and negative reviews can give great insights. The shop could combine this data with the shop assistant schedule to see how each assistant performs.

5. Natural language processing: sentiment analysis

An explanation that could be provided to test and evaluate the model is by addressing what and how much certain words contributed to the classification. In these examples, the amount of transparency highlights how important a certain word was for the classification of the review. Behind the scenes, the AI system provides a probability score to each word and how it contributes to the prediction. So we can see that in a positive review, the words 'passionate', 'knowledgeable', and 'appreciate' were important. Similarly, we can find the words 'expectations', 'didn't', and 'not' contributed to the review being classified as negative. In this way, we can understand the AI's decision-making, and clarify why the model works as it does.

6. Let's practice!

Now that we've seen case studies of how XAI aids in making AI systems understandable, we will apply our knowledge to some exercises.

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