Explain your Deep Models to Users, Regulators, and Other Stakeholders
Users must have a clear understanding and complete trust to work efficiently with AI-driven applications.
Deep Models Explanations are Necessary
Provide clear, comprehensible insights that ensure all stakeholders understand the model's decision-making processes, fostering confidence in deep learning based applications.
Trusted Deployment
Facilitate collaboration between business owners and data scientists, and encourage user adoption through clear and comprehensible insights accessible to both technical and non-technical users.
Regulatory Compliance
Achieve robust, ethical, and accountable DL models that meet regulations (AI Act, RGPD... and industry specific ones) thanks to precise, transparent, and auditable documentation.
Risk
Mitigation
Identify risk factors using "what if" scenarios and extensive analysis of model weaknesses for legal, certification, insurance, or financial departments.
Explainability Features
Explain the Model from Inside
Once an Xpdeep model is trained, you immediately have access to several explanations through the interactive interface and the API. Discover all the decisions deployed by the model to achieve the target task, the factors involved in each decision and their importance, and details for each predictive regions. This helps to understand the model’s functioning, evaluate its strengths and weaknesses and optimize certain features, decisions, or groups of samples. Xpdeep is also able to explain existing models without altering their architecture, which is crucial for large
models like LLMs.
Explain Generated Inferences and What-If Scenarios
To analyze and control future predictions, discover all the decisions involved, the contributing factors and their importance, their robustness and reliability. Explanations can be generated for individuals (local inferences) or groups (semi-local inferences). Local inferences are the only explanations provided by post-hoc methods.
Explain tabular, temporal, text and image data
Get explanations for various data types: tabular, temporal, text, and image data.
Xpdeep is the only solution to explain natively time series data. It lets you understand which sensors is necessary for the model and the ones that can be removed. It shows which part of the signal is contributing to model decisions. Thus it creates multiple savings opportunities, both from at the compute and physical world levels.
Explain Past Inferences for Incidents Review
Explain past predictions for auditing and investigating critical scenarios and incidents, simplifying regulatory compliance, and minimizing financial and legal risks. This functionality is essential for post-mortem analysis, ensuring that every decision made by the model can be understood and justified.
Simplify Regulatory Compliance
Simplify regulatory compliance by getting tools to analyze, document, and prove deep models' workings. This documentation is crucial for adhering to regulations and demonstrating accountability, minimizing financial and legal risks associated with AI integration.
Explain Model Errors
Business users can independently understand how and why the model arrived at its conclusions. This insight facilitates effective communication with AI developers for swift model improvements. Additionally, understanding the reasons behind specific errors helps maintain trust in the overall model, as users can see that errors are identifiable and correctable rather than indicative of fundamental flaws.
Better Experience
Classic Deep Models
Developers:
❌ I have no idea how to make it right...
❌ What did it do?
❌ What are its strengths and weaknesses?
End Users:
❌ How does it conclude that?
❌ Why is it wrong?
❌ How can I trust it?
Xpdeep Models
Developers:
✔️ I talk with experts to correct the model
✔️ I understand what happened
✔️ I verify and eliminate weaknesses
End Users:
✔️ I understand its conclusion
✔️ I know what needs to change to make it right
✔️ I trust the actions and decisions
Explain with no compromise on performance
Xpdeep is the first deep learning framework to provide full explainability with no loss of accuracy or time.
Integrate Xpdeep to develop faster better models.