Understand and explain
your ML models
Human oversight and explainability for any ML model.
The opportunity
Responsible AI enables you to
get more out of your algorithms
You are not limited to just the prediction, but can understand the full reasoning of the AI.
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Regulation
Comply with AI regulations to prevent fines up to €15 million or 3% of annual worldwide revenue.
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Reputation
Uncover hidden bias or discrimination to protect against unwanted prediction outcomes.
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Results
Accomplish more with complex models instead of simple ones, and get them in production faster.
The solution
Benefits of
Interactive visualization offers unique advantages for Responsible AI.
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Compliance
Support regulatory compliance (e.g., with AI Act, GDPR) through Human Oversight of even the most complex models.
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Visually uncover bias
Quickly see model strategies and bias through the unique, interactive exploration of model behavior.
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Streamline AI Governance
Standardize and accelerate model risk assessments, enabling faster time-to-market of AI solutions.
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Data Science productivity
Reduce time and training required for Data Scientists to explain models.
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Competitive advantage
Gain a competitive edge through deployment of more complex models without sacrificing explainability.
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Fast and cost-efficient
Our proprietary approach saves time and compute-resources compared to traditional explanation methods.
The Human-Oversight platform
untangles
complex ML models
Step 1 – Zero-effort setup
Create a project effortlessly
Xaiva seamlessly integrates into your current setup. You can simply create a project through an online wizard (your data is never sent to our servers), or choose one of our many integrations.
Step 1 – Zero-effort setup
Create a project effortlessly
Xaiva seamlessly integrates into your current setup. You can create projects directly from Python, or through integrations with your current setup (e.g., Azure, Dataiku).
Step 2 – Analyze your models
Understand ML models
Xaiva will immediately generate a model assessment to understand the human impact of your models, and generates dashboard for in-depth exploration and analysis of predictions.
Step 2 – Analyze your models
Understand ML models
Xaiva highlights whether your model employs different strategies to predict the same class. This helps to identify bias, and provides a simple, high-level model description.
Step 3 – Report to others
Explain predictions to stakeholders
Through analysis, you can understand your models, and trust that the explanations are correct. Generate simple reports you can share with customers, regulators and management.
This way, you can adhere to regulations, avoid negative publicity due to unjust ML decisions, and make quicker prediction-based decisions.
Who does it help?
Explanations throughout
the ML lifecycle
Data Scientist
DS Manager
Customer
Business User
Risk & Compliance
Regulator
Management
Case studies
Explainable AI for Insurance
Download our case study document to learn how we were able to provide transparency into complex models and reduce potential risks with the Xaiva Human Oversight platform.