Explainable AI (XAI) refers to methods and techniques that make the inner workings of artificial intelligence systems understandable to humans.
While Activepieces does not natively provide explainability features, it can connect to external tools and services that generate explanations for AI outputs, making it possible to embed transparency into automated workflows.
Explainable AI is an area of artificial intelligence research and practice focused on making AI decisions transparent and interpretable.
Traditional machine learning and deep learning models (particularly large language models and neural networks) are often considered “black boxes” because they produce outputs without clear insight into how decisions were made.
XAI aims to solve this problem by providing explanations that humans can understand.
These explanations can highlight which factors influenced a decision, how data was weighted, or why a particular outcome was chosen. By doing so, XAI helps make sure AI systems are trustworthy, fair, and accountable.
In Activepieces, while the platform itself does not perform explainability, it can integrate with APIs or tools that offer explanations.
For example, a flow could use a connected XAI service to interpret why a model classified a lead as “qualified” or flagged a financial transaction as suspicious.
Explainable AI works by applying models, frameworks, or techniques that translate AI processes into human-readable formats. In Activepieces, the workflow might look like this:
This approach makes sure workflows include both automation and accountability.
Explainable AI is important because it builds trust, ensures compliance, and reduces risks associated with AI-driven decisions. Without explainability, businesses risk adopting AI outputs blindly, which could lead to errors, bias, or regulatory issues.
Key reasons it matters include:
For Activepieces, explainable AI is not a built-in feature but can be integrated into flows. This flexibility allows organizations to combine automation with transparency, ensuring responsible AI adoption.
Explainable AI applies in industries where decisions must be accountable. Examples in Activepieces include:
These use cases demonstrate how explainable AI complements automation by adding clarity.
Explainable AI in automation refers to the integration of tools and techniques that make AI-driven decisions interpretable. It allows humans to understand the reasoning behind outputs in automated workflows.
It is important because it provides transparency, builds trust, and ensures compliance with regulations. Businesses using explainable AI reduce risks of bias, error, and blind reliance on AI models.
Activepieces supports explainable AI indirectly by integrating with external tools and APIs that provide explanations. Workflows can combine AI outputs with explanations, ensuring both automation and accountability.
