Time to read: 5 minutes 50 seconds | Published: October 1, 2025
Explainable AI What is explainable AI, or XAI?
Explainable AI is a set of processes and methods that allows users to understand and trust the results and output created by AI’s machine learning (ML) algorithms. XAI provides the explanations accompanying AI/ML output to address concerns and challenges ranging from user adoption to governance and systems development. This "explainability" is core to garner the trust and confidence needed in the marketplace to spur broad AI adoption and benefit. Other related and emerging initiatives include trustworthy AI and responsible AI.
How is explainable AI implemented?
The U.S. National Institute of Standards and Technology (NIST) states that four principles drive XAI:
- Explanation: Systems deliver accompanying evidence or reason(s) for all outputs.
- Meaningful: Systems provide explanations that are understandable to individual users.
- Explanation accuracy: The explanation correctly reflects the system’s process for generating the output.
- Knowledge limits: The system operates only under conditions for which it was designed or when its output has achieved sufficient confidence levels.
NIST notes that explanations may range from simple to complex and that they depend upon the consumer in question. The agency illustrates some explanation types using the following five non-exhaustive sample explainability categories:
- User benefit
- Societal acceptance
- Regulatory and compliance
- System development
- Owner benefit
Why is explainable AI important?
Explainable AI is a crucial component for growing, winning, and maintaining trust in automated systems. Without trust, AI, and especially AI for IT operations (AIOps), won’t be fully embraced, leaving the scale and complexity of modern systems to outpace what’s achievable with manual operations and traditional automation.
Trust makes "AI-washing" (implying that a product or service is AI-driven when AI’s role is tenuous or absent) apparent, helping both practitioners and customers with their AI due diligence. Establishing trust and confidence in AI impacts its adoption scope and speed, which in turn determines how quickly and widely its benefits can be realized.
When tasking any system to find answers or make decisions, especially those with real-world impacts, it’s imperative that we can explain how a system arrives at a decision, how it influences an outcome, or why actions were deemed necessary.
What are the benefits of explainable AI?
The benefits of explainable AI are multidimensional. They relate to informed decision-making, risk reduction, increased confidence and user adoption, better governance, more rapid system improvement, and the overall evolution and utility of AI in the world.
What problem(s) does explainable AI solve?
Many AI and ML models are opaque and their outputs unexplainable. It’s pivotal to the trust, evolution, and adoption of AI technologies to expose and explain why certain paths were followed or how outputs were generated.
Shining a light on the data, models, and processes provides insight and observability for system optimization using transparent and valid reasoning. Most importantly, explainability enables easier communication and subsequent mitigation or removal of flaws, biases, and risks.
How explainable AI creates transparency and builds trust
To be useful, initial raw data must eventually result in either a suggested or executed action. Asking a user to trust a wholly autonomous workflow from the outset is often too much of a leap, so it’s advised to allow a user to step through supporting layers from the bottom up. By delving back into events tier by tier, the user interface (UI) workflow peels back the layers all the way to raw inputs. This facilitates transparency and trust.
Ideally, a framework digs deep enough to satisfy domain expert scepticism while also enabling novices to search as far as their curiosity goes. This helps establish trust among both beginners and seasoned veterans and enables increased productivity and learning. This engagement also forms a virtuous cycle that can further train and hone AI/ML algorithms for continuous system improvements.
How to use explainable AI to evaluate and reduce risk
Data networking, with its well-defined protocols and data structures, means AI can make incredible headway without fear of discrimination or human bias. In this way, applications of AI can be well bounded and responsibly embraced when tasked with neutral problem spaces such as troubleshooting and service assurance.
It’s vital for your vendor to answer some basic technical and operational questions to help unmask and avoid AI washing. As with any due diligence and procurement efforts, the level of detail in the answers can provide important insights. Responses may require some technical interpretation but are still recommended to help ensure that claims by vendors are viable.
As with any technology, engineering and leadership teams set criteria to evaluate proposed purchases and make related decisions based on evidence. To reduce risk and aid with due diligence, here are a few sample questions for AI/ML owners and users to ask:
- What algorithms comprise and contribute to the solution?
- How is data ingested and cleaned?
- Where is the data sourced (and is it customized per tenancy, account, or user)?
- How are parameters and features engineered from the network space?
- How are models trained, re-trained, and kept fresh and relevant?
- Can the system itself explain its reasoning, recommendations, or actions?
- How is bias eliminated or reduced?
- How does the solution or platform improve and evolve automatically?
Additionally, pilots or trials on AI services and systems are always recommended to validate promises or claims.
Explainable AI in action at HPE Networking
The responsible and ethical use of AI is a complex topic but one that organizations must address. HPE’s Mist AI Innovation Principles guide the use of AI in our services and products. We also have extensive documentation regarding tour AI/ML and AIOps approach. This includes tools which help detect and correct network anomalies while improving operations, such as AI data and primitives, AI-driven problem solving, interfaces, and intelligent chatbots.
XAI can come in many forms. For example, HPE Networking AIOps capabilities include performing automatic radio resource management (RRM) in Wi-Fi networks and detecting issues, such as faulty network cables. Using advanced GenAI and AI agents, operations can selectively enable self-driving, autonomous actions from the Marvis Actions Dashboard once trust in the actions taken and resulting outcomes have been established.
At the core of Mist is the Marvis AI engine and Marvis AI Assistant. Marvis AI redefines how IT teams interact with and operate their networks. With the integration of agentic AI, Marvis AI can reason, collaborate, and act across complex environments, bringing the vision of the Self-Driving Network closer to reality.
A component of the Marvis AI Assistant is the Marvis Conversational Interface, which uses advanced LLMs, generative AI, and NLU/NLP to let IT teams ask questions in natural language and receive clear, actionable answers. It understands user intent, engages specialized agents, and orchestrates multi-step workflows to diagnose issue and, when authorized, autonomously resolve problems. Generated reports summarize actions taken and outcomes realized to document value and build trust with users. This combination of conversational intelligence and automation empowers IT teams to operate more efficiently, reduce resolution times, and focus on strategic initiatives that drive innovation.
Explainable AI FAQs
What is meant by explainable AI?
Explainable AI is a set of processes and methods that allow users to understand and trust the results and output created by AI/ML algorithms. The explanations accompanying AI/ML output may target users, operators, or developers and are intended to address concerns and challenges ranging from user adoption to governance and systems development.
What is an explainable AI model?
An explainable AI model is one with characteristics or properties that facilitate transparency, ease of understanding, and offer the ability to question or query AI outputs.
Does explainable AI exist?
Yes, though it’s in a nascent form due to still-evolving definitions. While it’s more difficult to implement XAI on complex or blended AI/ML models that have a large number of features or phases, XAI is quickly finding its way into products and services to build trust with users and to help expedite development.
What is explainability in deep learning?
Deep learning is sometimes considered a "black box", which means that it can be difficult to understand the behavior of the deep-learning model and how it reaches its decisions. Explainability seeks to facilitate deep-learning explanations. One technique used to explain deep learning models is Shapley (SHAP) values. SHAP values can explain specific predictions by highlighting features involved in the prediction. There is ongoing research in evaluating different explanation methods.