PAI provides responsible AI tools for fairness analysis, error analysis, model interpretability, security guardrails, and secure model training to help you build safe, fair, and trustworthy AI systems.
Build AI systems that are safe, fair, transparent, and aligned with ethical standards. PAI provides tools and best practices that cover the entire model lifecycle, from data preparation and training to evaluation and deployment.
Core principles
PAI's responsible AI approach is built on the following principles:
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Fairness: Identify and mitigate bias to ensure equitable treatment across all user groups regardless of sensitive attributes like gender, race, or age.
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Safety and robustness: Detect and prevent harmful content, adversarial attacks, and unexpected behaviors that could cause harm.
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Transparency and interpretability: Make model decisions explainable so users understand how and why predictions are made.
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Privacy and security: Protect sensitive data during training and inference, prevent data leakage, and ensure secure model deployment.
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Accountability: Continuously monitor model performance, analyze errors systematically, and take corrective actions to improve models.
Capabilities
PAI provides responsible AI tools in the following areas:
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Capability |
Description |
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Fairness analysis |
Detect bias in model predictions based on sensitive attributes. Evaluate fairness metrics across demographic groups and generate actionable insights to correct bias. |
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Error analysis |
Systematically identify, categorize, and analyze model prediction errors. Understand error patterns, root causes, and take corrective actions to improve accuracy. |
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Model interpretability |
Explain model decisions using techniques like feature importance, SHAP values, and attention visualization. Help users understand which inputs drive predictions. |
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Safety guardrails |
Detect and filter harmful content, prompt injection attacks, and policy violations in real time. Protect LLM applications from adversarial inputs. |
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Secure model training |
Train models on sensitive data without exposing raw data using federated learning. Multiple parties collaboratively train models while keeping data on local devices. |
Get started
Select a scenario that matches your needs:
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Scenario |
Description |
Documentation |
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Analyze model fairness |
Detect bias in classification or regression models. Identify unfair predictions based on gender, race, age, or other sensitive attributes. |
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Analyze prediction errors |
Systematically identify error patterns, understand root causes, and improve model performance through iterative analysis. |
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Protect LLM applications |
Deploy safety guardrails to detect harmful content, prompt injections, and policy violations in real time. |
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Train on sensitive data securely |
Use federated learning to train models across multiple parties without sharing raw data, preserving privacy and security. |
Best practices for data security in federated model training |
Integration with PAI services
Responsible AI tools integrate with the following PAI services:
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Data Science Workshop (DSW): Run fairness analysis, error analysis, and model interpretability studies in Jupyter notebooks with the responsible-ai-toolbox.
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Elastic Algorithm Service (EAS): Deploy safety guardrail services to protect LLM applications in production environments.
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Machine Learning Platform for AI (PAI): Implement federated learning workflows to train models securely across distributed data sources.