Monitaur Launches GovernML to Manage AI Data Lifecycle

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Monitaur, provider of artificial intelligence (AI) management software, launched for general availability GovernML, the latest addition to its ML Assurance Platform, designed for enterprises committed to responsible use of AI.

Offered as a web-based, software-as-a-service (SaaS) application, GovernML enables enterprises to establish and maintain a record of model governance policies, ethical practices and model risks for their entire AI portfolio, CEO and founder Anthony Habayeb told VentureBeat.

As AI deployment accelerates across industries, so do efforts to establish regulations and internal standards that ensure fair, secure, transparent and responsible use of this often personal data, Habayeb said. For example:

Entities ranging from the European Union to New York City and the state of Colorado are finalizing legislation that codifies into law practices embraced by a wide variety of public and private institutions. to demonstrate compliance and protect stakeholders from harm.

“Good AI needs good governance,” Habayeb said. “A lot of companies have no idea where to start driving their AI. Others have a strong foundation of policy and enterprise risk management, but no real enabled operations around them. They lack a central place for their policies, evidence of good practice and collaboration between functions. We built GovernML to solve both.”

The importance of AI governance

Effective AI governance requires a strong foundation of risk management policies and close collaboration between stakeholders in modeling and risk management. Too often, conversations about managing risk from AI focus closely on technical concepts such as model explainability, monitoring, or bias testing. This focus minimizes the broader business challenge of lifecycle governance and ignores policy prioritization and enabling human oversight.

How would this registration system fit in with other business systems such as data management apps, legal risk management, security, etc.? Or does it necessarily need to fit in at an enterprise scale?

“Monitaur has robust APIs behind its platform that enable the push and pull of information,” Habayeb told VentureBeat. “To unlock the potential of a true enterprise SOR for model governance, a solution must be able to ‘work together’ with key organizations, systems, policies and data from other functions. Good AI governance should support connectivity between systems and transparency between departments and reduce rework where possible.”

Habayeb provided examples of use cases where an AI-related problem could become a major problem.

“Nowadays you don’t have to be an expert to understand that AI systems are biased; The question now is whether an organization can prove its efforts to limit the damage,” Habayeb said. “Was the data assessed for bias? Were the developers trained on ethical policies? Is the model optimized for the correct metric? Has legal signed? These are examples of important bias controls in the responsible AI governance lifecycle. GovernML guides companies in drafting and proving these and other critical policies. This not only reduces the chance of adverse events, but also reduces the legal, financial and reputational damage when they occur.

“People forgive mistakes; they don’t forgive negligence,” Habayeb said.

While there are foundations for risk management and model governance in some industries, their implementation is rather manual, said David Cass, former Federal Reserve banking supervisor and IBM CISO.

“We’re now seeing more models, of increasing complexity, being used in more impactful ways, in more industries that lack model governance experience,” Cass said in a media advisory. “We need software to distribute governance methods and execution in a more scalable way. GovernML takes the best of proven methods, adds to the new complexity of AI and enables software throughout its lifecycle.”

The rise and necessity of AI governance is not just a result of AI investment or AI regulation; it’s a clear example of a broader need for synergy across risk, governance and compliance software categories in general, said Bradley Shimmin, principal analyst, AI Platforms, Analytics and Data Management at Omdia.

“If we consider software as an industry in its own right and compare its regulation to that of other major sectors or industries, the impact-to-regulation ratio of software is an outlier,” Shimmin said in a media advisory. “GovernML offers a very thoughtful approach to the broader AI problem; it also puts Monitaur in an attractive position for future expansion within this much broader theme.”

GovernML manages AI ethics policy

The integration of GovernML into the Monitaur ML Assurance Platform supports a lifecycle AI governance offering that covers everything from policy management through technical monitoring and testing to human oversight. By centralizing policies, controls and evidence across all advanced models in the enterprise, GovernML enables responsible, compliant and ethical AI programs to be managed.

The new software enables business, risk and compliance and technical leaders to:

Create a comprehensive library of governance policies tailored to specific business needs, including the ability to immediately leverage Monitaur’s proprietary controls based on AI and ML audit best practices. Provide centralized access to model information and evidence of responsible practices throughout the model lifecycle. Integrate multiple lines of defense and an appropriate segregation of duties into a compliant, secure registration system. Gain consensus and drive cross-functional alignment around AI projects.

Monitaur is based in Boston, Massachusetts. To learn more about GovernML, go here.

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