Explainable Artificial Intelligence: Simple or Complex AI Model - Which is Better for Business?
Oct 22, 2024
Explainable AI can be a remedy for your business. It will show what is behind the complicated decisions of artificial intelligence. It will guard you from misunderstandings, biases, and potentially risky mistakes made by AI.
But what if simple AI models could still be better?
Read this article and find out how to make the right choice between simple and complex AI models. Which part does explainable AI play in this decision?
Explore:
- What is Explainable AI, and what do we mean by saying “black box problem”?
- Simple AI Models
- Complex AI Models
- Which is better for business?
What Is Explainable AI
AI is a buzzword and is used now in many industries. We believe its decisions, calculations, or predictions. However, is everything really as good as it seems? Can we truly rely on AI?
The answer is: it depends.
In cases like medicine, for example, one little mistake could cost lives. Bias in the system could run unnoticed and lead to unpredictable outcomes. The complex structures behind the complex AI’s logic are called a “black box” problem.
This term means the decision-making process is hidden and difficult to understand. Just like a black box. And this is the place where Explainable AI (or XAI) comes in. XAI opens the box and shows the reasoning. For industries that require transparency and trustworthiness, this could be a critical factor.
The goal of XAI is to show how black box models work and in what ways AI makes decisions. Basically, XAI tries to answer the following questions:
- What factors influenced the decisions of AI?
- What were the potential biases and gaps?
- How will AI behave in similar scenarios?
- Can this decision be reproduced or validated?
In the heart of XAI are ML algorithms. Using them helps XAI to bridge the gap between human comprehension and AI’s logic.
Why do we need Explainable AI?
XAI is based on three factors, namely explainable data, explainable predictions, and explainable algorithms.
Data explainability: Data that feeds an AI model should be explainable and, basically, understandable. If such information is biased, incomplete, or wrongly labeled, its predictions are likely wrong.
Explainable predictions: With the help of XAI, you are able to know what stands behind its predictions.
Explainable algorithms: This refers to the transparency of the actual model and algorithms the AI uses to arrive at its decisions. For example, deep learning neural networks are complex and could require techniques like SHAP values or LIME to help explain how each part of the model contributed to the output.
AI could help to understand complex AI models. But does this mean simpler ones are better? Now, let’s proceed to the description of simple & complex AI models in order to define which one could fit your business.
Simple AI models
Simple models are straightforward and interpretable. Examples of this are linear regression or decision trees. They have simple logic and are built on simple algorithms. This means the need for XAI is not so high. Basically, you can understand how input variables affect outcomes.
Complex AI models
Complex AI models are intricate but powerful. They often involve multiple layers of computations and abstract representations, helping them solve sophisticated tasks and detect intricate patterns in data.
The problem with complex AI models is their lack of interpretability. This is a black box mentioned above. Businesses relying on AI decisions should have confidence — they can trust them and explain each and every step they take (according to artificial intelligence's suggestions).
Using XAI could help understand things better, but it's often related to higher costs. For example, using XAI techniques like LIME means developers need extra components to explain the model's decisions. This also means you might need to hire XAI specialists or invest in retraining your existing team, and you'll likely need to allocate more time for development.
Overall, the cost of XAI could vary, reaching even $500,000. However, it's essential to recognize that the value derived from implementing Explainable AI can far exceed the initial costs.
Which is Better for Business?
The choice between easy and complicated AI models would depend much on what your specific business needs and goals are.
Here's a breakdown that will help you decide with ease:
- For businesses that put trust and compliance first: If your business works in a regulated environment where decisions have to be explainable — for instance, healthcare, finance, or insurance — the simpler models in AI are safer bets.They offer transparency and can easily meet regulatory requirements. These models might be less powerful but can build trust with stakeholders.
- For data-driven businesses in fast-moving industries: If your business belongs to fast-moving industries and is driven by analyzing big sets of data in order to make predictions, then it may need more complex AI models in order to unfold its competitive advantages. These models can help obtain deeper insights through the accuracy and scalability of these complex AI models — even if they are not explainable — they could unlock such a deeper insight that provides a competitive advantage in a competitive market.
- For hybrid solutions: Perhaps the best of both worlds is a hybrid approach: make complex models for high-stakes predictions, and make simple models provide explainability and validation of those predictions.
Does XAI sway the decision toward the complex AI models?
The answer is: not in all cases. Understanding patterns does not always equate to comprehending the underlying mechanisms. XAI provides a layer of explanation but does not always simplify the complexity inherent in the model itself.
Summing up…
The choice between simple and complex AI models is nuanced and context-dependent. This is always a balance between business needs and transparency.
Undoubtedly, XAI can add value, especially in high-stakes environments. However, it also introduces costs in terms of both resources and time. What’s more, building and deploying AI solutions requires solid expertise. To navigate the complexities of AI models, consider working with experienced developers.
Devler.io could help you assemble a team of AI experts who can guide you through the intricacies of model selection and implementation.