What is AI Governance?
AI Governance is the set of frameworks, rules, and best practices to ensure responsible adoption and usage of artificial intelligence. This framework encourages organizations to curate and use bias free data, consider societal and end-user impact, and produce unbiased models; the framework also enforces controls on model progression through deployment stages. AI Governance is critical for organizations to realize the maximum value out of AI projects while mitigating risks. It enables organizations to not only develop AI projects in a responsible way, but also ensure that there is consistency and transparency across the entire organization.
H2O AI Governance Framework
Gain maximum value out of AI projects and develop consistency for organization-wide adoption of AI.
H2O.ai's AI Govenance Framework recommends four stages and a total of 11 topics. Organizations are encouraged to adopt the topics and processes most relevant to their unique needs.
Organizational PlanningStage 1
Use Case PlanningStage 2
AI DevelopmentStage 3
AI OperationalizationStage 4
Develop an AI Governance Program to ensure your team is compliant, prepared, and aligned on laws, regulations, policies, and organizational processes.
Establish a business value framework, pre-approved set of AI tools, and model usage documentation.
Build models with best practices on handling data, modeling, explanability, validation, fairness, and robustness.
Operate AI with agility and confidence by tracking experiment lineage, monitoring deployment metrics, and managing decision-making and incident-response pipelines.
AI Governance for Enterprises
Organizations of all sizes, across all industries and domains are leveraging artificial intelligence (AI) technologies to solve some of their biggest challenges around operations, customer experience, and much more. Access the position paper below on a brief introduction to AI governance, which is a framework designed to oversee the responsible use of AI with the goal of preventing and mitigating risks. Having such a framework will not only manage risks but also gain maximum value out of AI projects and develop consistency for organization-wide adoption of AI.
Responsible AI incorporates best practices for people and processes as well as technology. Beyond regulatory compliance, companies deploying machine learning in production have real business interests in understanding their models and trusting that models will perform as expected and that models are not discriminatory.