Explainable AI (XAI) refers to methods and technologies that help people understand how AI models work and how they arrive at predictions, decisions, or outputs. By providing insights into model behavior and the factors influencing individual predictions, XAI can make otherwise complex or opaque AI systems more understandable and transparent.
At SHER DeepAI, we develop XAI technologies that help different stakeholders understand how AI models arrive at predictions and decisions. Our aim is to make XAI easily accessible and applicable across different AI systems, while providing explanations that are human-understandable and tailored to the needs of different stakeholders.
Explainability is an important component of Trustworthy AI, but it is only one part of the broader challenge of building and operating trustworthy AI systems.
Through DeepTai, we are developing capabilities that connect explainability with AI governance, observability, human oversight, and AI security, supporting organizations in operationalizing Trustworthy AI.
Our technology development focuses on providing meaningful insights into three key areas:
| Model Behavior | Feature Influence | Individual Predictions |
|---|---|---|
| Understand how AI models behave and which patterns and relationships influence their outputs. | Analyze how individual features contribute to model predictions and decisions. | Understand why a model produced a particular prediction or output for a specific case. |
Different stakeholders need different types and levels of explanation. An AI engineer, risk manager, business user, or decision-maker may need different information from the same AI system.
Our approach therefore focuses on making XAI insights:
| Human-Understandable | Stakeholder-Centric |
|---|---|
| Translating complex model explanations into information that people can interpret and use. | Adapting explanations to the role, context, and information needs of different stakeholders. |
The objective is to move from simply generating explanations toward providing explanations that can support understanding, validation, decision-making, risk management, and human oversight.
Our work in Explainable AI combines research, experimentation, and technology development. We have developed different XAI technologies to explore and advance approaches for making AI systems more understandable, transparent, and accessible.
| DeepXAI Toolbox 1.0Model-Agnostic XAI Technology | DeepXAI-4-LLMXAI Technology for Large Language Models | DeepXaiExplainability Studio within DeepTai |
|---|---|---|
| Developed as our first XAI prototype, DeepXAI Toolbox 1.0 combines multiple explainability techniques in a unified framework with plug-and-play integration that does not require model redesign or retraining. It provides role-based explanations for technical and non-technical users through an accessible, chat-based interface. | Developed as an experimental XAI prototype for Large Language Models (LLMs), DeepXAI-4-LLM combines human-understandable explanations, risk and quality insights, and XAI-driven prompt optimization through a simple chat-based interface. Its model-agnostic architecture is designed to work across a broad range of LLMs, including GPT, Claude, Gemini, Llama, and Mistral. | DeepXai is our Explainability Studio within the DeepTai platform. It integrates different explainability methods with our human-understandable and stakeholder-centric approach to provide meaningful insights into model behavior, feature influence, and individual predictions. |
| Explore DeepXAI Toolbox → | Explore DeepXAI-4-LLM → | Explore DeepXai → |
Interested in our XAI technologies, potential applications, or collaboration opportunities? We would be happy to connect and discuss how our technologies could be applied to your use case.