Exposing the Black Box: XAI
How XAI Maintains Human Trust in an AI World
It’s 2026 and the AI revolution is fully at hand. AI is now an everyday tool used by both consumers and businesses of all types. Yet, one issue continues to nag the technology: AI’s well-known propensity to make mistakes. While of concern for all users, businesses especially face potential grave business and legal ramifications from blindly accepting AI-driven results. The AI industry is, of course, well aware of this. One answer is the rise of explainable AI (XAI) technology. Simply put, XAI aims to solve AI’s “black box” issue by coming up with technical means to explain to humans how an AI algorithm arrived at a result. For businesses adopting AI, implementing XAI is an important step in maintaining trust in their AI systems.
XAI may also be indispensable in meeting the increasing number of AI regulations. The most notable AI regulation (as of time of writing) is the European Union (EU) AI Act and several states such as Colorado have adopted AI-related regulations. For example, Article 13 of the EU AI Act states that “High-risk AI systems shall be designed and developed in such a way as to ensure that their operation is sufficiently transparent to enable deployers to interpret a system’s output and use it appropriately.”
What is XAI?
The AI systems that most people are familiar with are generative AI systems based on large language models (LLMs). LLMs leverage neural network technology to parse and create results from their vast amounts of training data. An issue with neural networks is that typically it can be difficult to explain —even for developers—just how the AI came up with a certain result. XAI tries to make AI explainable by building technologies into AI systems to help humans understand how an AI system came up with a certain result.
Decision tracing is one technical method which looks to audit the system’s decisions. DeepLIFT (Deep Learning Important Features) is one technique for this which compares the changes in how AI model neurons are operated to a baseline input to determine the output differences.
Local approximation is another method. This slightly changes the inputs for decisions and then creates an interpretable model for explanation. One technology which does this at a local decision level is LIME (Local Interpretable Model-Agnostic Explanations).
There are also methods that work with the human element rather than relying on XAI technologies. For example, an AI system can just show what the prompting was that came up with the result to the user. If the user has doubts as to the results, they can try changing their prompts.
Putting XAI into practice: CereHive
As a company that extensively uses AI to help companies streamline their recruitment processes, our partners at CereHive view explainability and XAI as indispensable to help their customers maintain trust in their results. They view explainability and XAI as a core competitive differentiation piece to their work. For example, CereHive uses AI to sort the flood of resumes that typically result from a job posting into four grades per the perceived suitability of the candidates for the position. It’s important to note that any decisions on who to actually interview for the position are left to the hiring managers and not automatically determined by the AI.
For the hiring manager to have confidence in their decisions, they need to trust these results. To support this, CereHive provides an explanation for each of the results explaining how it determined the candidate met criteria set out for the position. They also explain why any candidates would be considered not a good fit for each open position, so that no one is left wondering why they weren’t selected for an interview. XAI helps in creating these explanations. CereHive uses a number of third-party AI model providers as well as its own technology to create its results. Each model provider has an explainability implementation that CereHive leverages.
Generative AI is still a relatively young technology and is rapidly advancing. XAI techniques will also continue to progress. For companies that put AI into practice, it’s important to put XAI into production now and continue to iterate as the technology grows even more powerful and useful.
This article was written by Phillip Keys on January 27th, 2026.