Artificial intelligence (AI) often operates under mysterious circumstances that are difficult to observe. In most cases these operations are executed within a black box that computer scientists are unable to look into, causing them to question how a machine has made its decisions and output. 

To counteract this issue, engineers developed a system called Explainable AI (XAI) that can help observers interpret a machine’s actions. This has helped programmers create machines that can be held accountable in the context of law, ethics, and accuracy. However, there are methods of XAI, so it’s important to know the different techniques being used. 

The Need for Explainable AI

A major reason that XAI exists is trust, while machine learning algorithms are capable of producing highly accurate outputs there is still a major concern about its accuracy. Without the ability to check a machine’s work, users and developers alike worry that overreliance on these systems could lead to a rising list of failures. 

Transparency is also needed to ensure that the machine is working properly. Without a clear glimpse of the machine’s processing, scientists can’t evaluate what parts of the algorithm need improvement. This hindrance can create a huge delay in deployment and prevent teams from launching their product too.

XAI is also necessary for the development and facilitation of stronger regulatory compliances. AI programs are still fairly new to the public and legislators need to know what exactly is happening inside an AI program before they can develop laws. Moreover, these frameworks can also help ensure that AI models are following a certain set of rules and stipulations when it comes to algorithm output. 

Techniques for Creating Explainable AI

XAI can be created in various ways using different techniques. These different methods all serve specific purposes that are meant to improve a model’s transparency. They include:

• Feature Visualization: These Models can inform an observer about which inputs are most important to an algorithm’s decision-making. Features can include any form of input such as a word, image, or real-time data. 

• Model Simplification: This involves reducing the complexity of an AI model to make it easier for developers to determine how outputs were created. By scaling a system down, models also become easier to audit and analyze. 

• Local Interpretable Model-agnostic Explanations: LIME offers a granular explanation of a system’s decision-making process by creating a simpler model that follows the primary algorithm outputs. 

• Shapley Additive Explanations: Game theory is applied to this method, attaching values to each feature input so they can be measured in the output and ensure that all inputs are being used evenly. 

Methods to Implement Explainable AI

Choosing the right XAI system can be challenging because it requires programmers to balance their algorithm between efficiency and transparency. This balancing act can be difficult to master, especially for deep learning models that are highly complex. By installing too much XAI, developers risk losing key features of their AI algorithms. 

Careful feature selection is one way that developers can enhance XAI systems without risking model complexity. Features that are easy to identify and relevant to a task can help XAI systems identify their use in outputs more readily while also removing irrelevant or noisy features. 

Depending on the programming language used, certain libraries and frameworks can also be found that offer XAI visualization tools. This can help reduce the burden that XAI creates by making it easier to implement methods that are built directly into the model and open-source. 

Challenges in Implementing Explainable AI

One of the primary challenges in using XAI is the lack of standardization. AI algorithms exist for a myriad of purposes that range from social media suggestions to business forecasting and teaching, causing the range of XAI programs to vary as well. 

This makes it difficult to develop an XAI program that can enhance transparency while also maintaining complexity for a model. It also means that explanations of a machine’s decision-making can be difficult to understand with different forms of reasoning being given to observers depending on the XAI program being used. So, it’s important to test XAI systems routinely through development to ensure that they will remain useful by the time a model is ready for deployment and commercial use.