Explainability
Understand AI decisions, build user trust and prevent existing models from developing undesirable biases.
User-centered explanations of AI
Explainability that goes beyond the technical, providing explanations and interfaces that are tailored to all types of AI models, business contexts and users.
-
Powerful technology
Bring transparency to your models with robust explainability that goes beyond what is available in academic or open-source Explainability tools and libraries.
-
-
Fairness analysis
Explore, identify and mitigate for inaccurate or unfair models that have learned biases from poorly constructed datasets.
The Element AI Difference
Human-centric and state-of-the-art Explainabilty that can easily interface with a range of different models and be tailored to specific needs.
-
Transparent
Avoid black-box models by providing clear explanations tailored to different types of users, technical and business alike.
-
Fair
Make fairness a first-class citizen in AI models by using our expertise to identify biases that could harm your users.
-
Flexible
Our methods can be adapted to provide you with effective explanations for any model in the EAI OS and for any business context.
Explainability at work in Element AI products
Element AI Knowledge Scout enables natural language search on enterprise data and leverages user behavior to capture previously tacit information. Built-in explainability shows how the AI understood the question and came up with its results, building trust between the user and the system.
Book a meeting