Explainable AI (XAI) is an important method within Artificial Intelligence (AI) that aims to make an AI model’s decision-making interpretable to humans. Normally, a Deep Learning model’s decision-making process is located in a “black box” that hides the reasoning and logic of the decision from developers, making it difficult to evaluate an algorithm's accuracy.

Without being able to explain itself, AI programs can create many liabilities for itself, the development team, and the consumers themselves because there is a lack of trustworthiness. By not knowing the model’s logic, it is difficult to create an AI program that can comply with local laws and regulations while avoiding biases at the same time. So why is Explainable AI important and how can it improve decision-making?

A Brief Overview of Traditional AI Models

Traditional Machine Learning models tend to rely on simple, rule-based systems that create outputs directly from inputs. However, Deep Learning models, and specifically neural networks, have multiple layers of nodes between the input and output layer that process information and it’s within these nodes that decision-making can become difficult to observe. 

However, a balancing issue arises when XAI is implemented into AI models where developers must choose between a model that is more accurate or one that is easy to interpret. This is because installing and using XAI requires a model to siphon off computational resources from its primary functions. 

The Need for Transparency in AI

For many AI models, especially those used on an enterprise-grade level, just being accurate isn’t enough. Users want to know how and why a model came to its conclusion so that they can weigh the output data against their own intuition. This creates a need for transparency as models become more advanced and reasons include:

• Trustworthiness: In certain scenarios, like a doctor prescribing medication to a patient, having trust in an algorithm is paramount because neither party will want to follow a prescription suggested by AI without knowing its reasoning for fear of being harmed by unknown side effects or complications. 

• Regulatory compliance: With AI products becoming more common, there has been a strong push for more dedicated laws and regulations centered around its use. In certain sectors like finance, these regulations will require transparency when it comes to financial advice and strategies.

• Ethical considerations: It is not uncommon for datasets to obtain biased information that is unknown to the developers and can lead to larger issues. By explaining its process, AI models can help programmers identify compromised datasets. 

Real-World Examples of XAI in Action

Since its inception, XAI has become a mainstay in many industries because of their need for transparency. A few examples include:

• E-Commerce: XAI can assist online shoppers by explaining recommendation feeds and how products were selected based off of their past purchases. 

• Banking: Applications for credit lines can be a rigorous process for some people who have complicated financial situations and XAI can help them understand why they were accepted or rejected. 

• Autonomous vehicles: Although self-driving vehicles have come a long way, they are still susceptible to collisions and IT teams can use the vehicles XAI to understand what caused a crash. 

Challenges and Considerations

As mentioned before, the largest problem with XAI currently is the balance between ability and transparency. As models become more advanced, XAI tends to slow them down and prevent them from being effective. This creates a conundrum where an AI product can’t be accurate enough for commercial use with XAI while also being in lower demand without XAI.

It’s also important to recognize that XAI is subject to being inaccurate or blatantly wrong just like the original content being output by the algorithm. Overly simplified explanations and human complacency on AI could lead to unwanted results where the problem is realized too late. 

Conclusion

Despite its current limitations, XAI is essential for the advancement of AI technology in general. It is an intersection between advanced AI capabilities and human interaction, giving us insight into just how the computer mind functions and without it, we would be left in the dark wondering if our efforts in AI research are sufficient. 

However, the balancing act between interoperability and accuracy continues to play out, it’s clear that XAI will continue to grow as more humans rely on the advent of generative AI and the seemingly endless amount of possibilities that it creates. Even more so, XAI plays an essential role in legal matters as well, providing attorneys and legislators with the information they need to develop safe regulatory frameworks for the future of commercial AI use.