Imagine hiring a team of trained AI engineers to help address a recurring problem raised by employees in the firm. Once the AI model is developed and incorporated, you notice the workers are not making use of the expensive investment.
How is it possible that something meant to resolve worker concerns in a safe and accurate manner is rejected by them? This reaction can be understood by explainable AI (XAI).
Also Read: Various Biases in AI and Their Solutions
Discover how explainable AI is the key to understanding the end user’s perspectives.
AI is rapidly sinking its teeth into every sector and industry. Critical sectors like manufacturing and healthcare need transparent and trustworthy technology to meet rising demand. Consumer trust is of utmost importance, and XAI helps uphold it.
Necessity of Explainable AI
Many AI models have a black-box nature, meaning the reasoning behind why it made the decision (irrespective of how accurate it is) is not understood.
Lack of transparency like this can create confusion and increase skepticism. This becomes particularly problematic in high-risk situations.
AI may be able to give an accurate diagnosis for an issue, but if it is not comprehensible by healthcare professionals, it is not effective. Additionally, medical data is riddled with different types of biases.
These biases can be mitigated with explainability. Properly understanding how the AI model came to the specific decision can help healthcare providers.
How to Approach XAI
There are many different methods to approach explainability, but let us look at 2.
Creating Understandable Models
The knowledge base of an AI engineer is significantly different from that of others. What is easy for them to understand could be complicated for others.
So, creating interpretable models that explain in detail through decision trees or other methods is a common strategy. However, complex tasks can be challenging to represent on such models.
Providing Explanation Post-Model Development
Another strategy is where the explanation is generated after the model has made its decision. It becomes useful for more complex models that can be approximated with simpler models for better context.
Hurdles and Future Outlook of Explainable AI
Currently, one of the primary limitations is a lack of agreement on its definition. Do the terms interpretability and explainability imply the same meaning to you? For some researchers, they do.
Along with this, finding the right balance between the model’s output and its (simple) explainability is a major concern. This raises further questions on whether other methods of testing transparency should be focused on.
With advancements in XAI and its discourse, the level of transparency should ideally increase. Additionally, a consensus can eventually be reached and guidelines generated to make explainable AI implementation ready.
Closing Thoughts
Explainable AI is a critical aspect of ethical AI deployment. End users must be able to understand why the AI model has made a decision and the reasoning behind it.
