Machine learning assurance - Enabling trust in data-driven systems
Machine learning assurance provides a risk-based approach to establish trust in systems that use machine learning (ML).
Machine learning assurance: purchasing and implementing machine learning systems
Interest in systems incorporating machine learning, artificial intelligence (AI) and other data-driven techniques is spiking, as organizations seek ways to do things better, faster and to push the boundaries of what is possible. Enterprises considering data-driven applications need evidence that the system will meet business needs, is fit for purpose, limits exposure to liability, and that decisions are based on unbiased information. How can you select a supplier of ML systems with confidence? Machine learning assurance can reduce risks in your organization.
Building and selling machine learning systems
While opportunities for companies building machine learning solutions are plenty, there are many challenges. Making the right decisions on algorithms, techniques, data and evaluation criteria is crucial.
The complexity of the data and of the training algorithms, coupled with a lack of specific standards and regulations, can make establishing trust in such solutions a difficult task. As a result, potential investors and customers often take a conservative stance or refrain from adopting such solutions until these have matured. How can you bring to market proven ML solutions?
Machine learning risk assessment
With the Recommended Practice DNV-RP-0510 Framework, stakeholders obtain a better understanding of machine learning risks. Through an evaluation of the design, development, testing and deployment of your machine learning model, this Recommended Practice (RP) allows you to identify and manage risks, increasing the likelihood of a successful outcome with a machine learning risk assessment. The RP provides sound guidance for machine learning risk assessment:
- assess the development process of machine learning
- assess the machine learning model
- assess the risks of using the model
DNV is recognized in the field of machine learning and in identifying, assessing and mitigating risks. Our framework helps you scrutinize decisions made by the developer, even if you lack the domain knowledge. With organized claims and documentation, you can assess the implications of using the machine learning system for the intended scope.
DNV’s assurance and risk assessment service for data-driven models
The machine learning assurance and risk assessment service covers the complete pipeline, from data collection and ingestion to data preparation, modelling, prediction and deployment. For each stage in the pipeline, aspects of high risk are identified along with possible mitigating actions. We help you with a framework that eases communication about a machine learning project’s risk to all stakeholders – it provides easy-to-understand information about complex machine learning and data science topics. Using a workshop and questionnaire-driven format, the service delivers:
- A detailed register of machine learning risk items and mitigating actions
- Feedback to your machine learning project team or data science vendor from DNV’s domain experts and data scientists
- Trust and assurance that the machine learning model will deliver the expected results
