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AI Governance for AI Governance Teams & Committees

Ensure the responsible, ethical, and compliant use of AI technologies across an organization, while maximizing effectiveness and mitigating risks

AI Governance Use Cases for AI Governance Teams & Committees

Compliance with Regulatory and Ethical Standards

Theses teams ensure that AI systems adhere to all applicable laws, regulations, and ethical guidelines is a top priority. This includes data privacy laws like GDPR, industry-specific regulations, and internally set ethical standards, to mitigate legal and reputational risks.

Transparency and Accountability

These teams emphasize the importance of making AI systems transparent and accountable. This involves maintaining clear documentation and audit trails for AI decision-making processes and outcomes, ensuring that AI actions can be explained and justified to stakeholders, regulators, and the public.

Risk Management and Bias Mitigation

Identifying and managing risks associated with AI deployments is crucial, including technological risks, operational disruptions, and risks of bias in AI models. Governance teams focus on implementing strategies and tools to detect, monitor, and mitigate these risks to ensure fairness, accuracy, and safety of AI applications.

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Frequently Asked Questions

How does ModelOp's AI governance software help in ensuring compliance with specific regulations like GDPR or HIPAA?

ModelOp provides a library of templates to define and enforce policies and workflows for a variety of regulations including HIPAA in the US, and GDPR in Europe. Additionally, ModelOp maintains an up-to-date and accurate library of regulatory templates for regulations and advisories as they are published. ModelOp enforces requirements on metadata, documentation, testing, ongoing monitoring, configuration, peer review, and approvals. Together, this enables ModelOp customers to quickly “become compliant” and easily “stay compliant.”

Can ModelOp's software integrate seamlessly with our existing AI and data analytics platforms?

Yes, ModelOp is built for integrations and automations with an organization’s existing technology stack and AI investments, making it easy to get started and to extend the value of existing investments. ModelOp has 50+ out-of-the-box integrations and has a RESTful API, which makes it easy to customize and connect with in-house or other proprietary systems.

What mechanisms does ModelOp offer for monitoring and mitigating biases in AI models across the enterprise?

Identifying and mitigating bias in a model is the responsibility of all stakeholders, and ModelOp enforces requirements on metadata, documentation, testing, serving configuration, peer review, approvals and process gates at any and all stages of the model life cycle. ModelOp’s automated reports, controls, and workflow engine improves effectiveness and efficiency, while removing redundancy and friction from a model’s “path to production.”

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