What if you could understand the reasoning behind your AI’s forecast? Try XAI!

"According to Mckinsey, organization that can build customer trust by using technologies like explainable AI can expect their annual revenue to grow by 10% or more."

AI…?

Black Boxes...?

Lacks Explainability....

The reasons behind the suggestions??

These are a few things that AI is being criticized for in every article. But what if there is a way to understand the AI brain???

In this article, we will uncover the technique that reveals the reasoning behind an AI model’s decision-making.??

You must have seen how significantly AI has been rising across different functions in various industries. However, there is no explanation for how these advanced models magically arrive at a specific output. Even the data scientists and engineers, who took part in the fine-tuning of the model could not explain the mysteries behind these outputs.?

The answer to this problem??

“Explainable AI”?

Explainable AI, also known as XAI, is a set of methods and tools designed to help users understand why an AI model produces an output. It will not only mention the different factors that went into consideration while generating the output but also quantify the contribution of each of these factors to the output.?

Note- You must note that Explainable AI is quite different than Interpretable AI. The former focuses on “why- reasoning behind the output” of the output whereas the later focuses on “how- Inner working of the AI model.”        

What Benefits does XAI brings to the table?

Trying to resolve the black box problem can bring many other benefits with it.

#1 Better interpretability of the result- As mentioned earlier, XAI lists various factors and their overall impact on the solution. This can be incredibly useful in interpreting the effect of adjusting the factors on the result.??

#2 Optimization and fine-tuning– If the AI model is not performing as expected, XAI can help interpret the reason behind the incorrect output. For example, analyzing the XAI output can reveal that the AI model is prioritizing the factors that are not important.?

#3 Mitigating Biases- XAI can help unveil the input factors that are leading to biases in the output. Not only that, but you also identify the extent to which it is influencing the business.?

#4 Regulation- Tasks that are sensitive like finance, health, judiciary, etc, are required that they have a comprehensive explanation of the AI result that has been used in the decision-making process. In many such case, it is mandated by the law to have these explainable.?

?How Applicable is XAI in your business function??

It’s important to understand that not all AI models need XAI for its explainability. Some models, like linear regression and decision trees, follow simpler structures that can be explained easily. On the other hand, more complex models with greater capabilities are difficult to interpret. Essentially, the more capable an AI model becomes, the more challenging it is to understand. This is where the need for XAI is most justified.?

Hence, whether you should implement XAI depends on your current AI model and your future requirements.??

Consideration while Implementing the XAI Technique

After you have decided on implementing XAI, the next step is choosing the XAI model. Selecting the model depends on the organization’s requirements, cost considerations, and available resources. Here are some of the points to consider.

#1 Explainability methods- These methods help understand how different features or data points influence a model’s prediction. There are local methods that explain single predictions while global methods explain overall AI predictions.?

#2 Complexity vs clarity- Simple XAI models are less resource-intensive but might be less accurate. While complex models require cost and resources to implement but are highly accurate.?

#3 Choosing the right tool- Some explainability methods work best with specific models providing more accuracy, while other methods can explain all models but with decreased accuracy.??

#4 Backpropagation vs Perturbation- In backpropagation methods, the XAI model traces the influence of the final output back to the input. Whereas the perturbation method modifies the input data and monitors changes in output.?

For more detailed understanding about XAI and its models, refer to the blog: Understanding the AI Reasoning with Explainable Artificial Intelligence (XAI)

XAI’s real world Industry example?

Wells Fargo improving loan rejection transparency with XAI implementation.?Wells Fargo implemented an XAI model called LIFE that gave the reasoning behind the AI’s decision to reject a loan application. It generates codes that represent different reasons for loan rejection. For example, a code might indicate a high debt-to-income ratio while another indicates the FICO score to be below the minimum requirement. Apart from these, their model considers anywhere from 40 to 80 of such variables when explaining rejection. This improved the transparency of their system and helped applicants understand the factors behind these rejections, potentially improving their chances of securing future loans.?

Emerging Use Cases for XAI Technology?

As XAI grows and matures, several applications have been emerging. Let us explore a few of these.

Healthcare???

While advanced AI can analyze vast amounts of data to virtually scan millions of potential drugs and their effects. XAI helps researchers understand the rationale behind AI’s suggestion. By factoring in their impacts, scientists can proactively adjust them to achieve the desired result.??

CPG?

AI has seen a multiple use throughout the CPG supply chain. And with an increased implementation of AI, more avenues for XAI are coming up. For example, in demand forecasting based on the Regression model, based on the graph, it might not be clear to the decision-makers the most dominating factors. What if they conclude that the changes in forecast are due to changing economic conditions???

However, XAI revealed that the impact of economic conditions is only 30% while the rise in competition had a 70% impact.?

Manufacturing?

AI in manufacturing is powerful but opaque. Using the reasoning generated by XAI, for example, it can reveal that a machine needs maintenance by also why it needs it. Such insights allow manufacturers to optimize processes, pinpoint quality issues, and gain great control.?

Challenges of implementing XAI

XAI does come with a whole lot of significant advantages.

Cost of Implementation- Developing and implementing XAI models can be highly resource and cost-intensive. This can act as a barrier for small organizations, as it requires expertise on XAI techniques and domain-specific knowledge to interpret their explanations.?

Highly process intensive- XAI is complex and demands additional computing power, which can lead to increased hardware requirements. Moreover, complex models might take a longer time to be analyzed, adding to increased processing time.?

Limited explainability- With the current level of XAI, it might be difficult to interpret highly complex models with multiple layers. This limitation means that some models may remain partially opaque despite using XAI techniques.?

The Road Ahead

The progress in AI has come with its own set of challenges, one of them being the lack of transparency. While many organizations are working on implementing Generative AI solutions, considering XAI capability can help them avoid several issues related to AI beforehand. By integrating XAI, organizations can ensure that their AI system is understandable, and improves trust in their decision-making process. Moreover, implementing Explainable AI early in the AI development process can help avoid complexities later, making a future-proof system.?

If you are curious about the implementation of Generative AI solution, download our free Ebook:?Fascination to Implementation: Are you truly ready for GenAI?

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