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Ai Governance is the process of defining policies and establishing a framework to guide the creation and deployment of Ai systems in an organization. Ai governance empowers organizations to operate with agility and complete trust.
Not only does Ai governance provide a consistent process an organization can follow for implementing AI Solutions, more importantly capturing and managing metadata on Ai models as part of Ai governance processes provides transparency into how Ai systems are constructed and deployed, a key requirement for most regulatory concerns. An Ai governance framework ensures models developed are explainable, transparent, and ethical.
Establishing and measuring key indicators is important in establising an Ai governance program. What gets measured gets improved.
These metrics are tailored to ensure robust oversight over an organization’s use of AI.
Data: This covers the data quality assessment and lifecycle of data (data lineage).
Security: This engulfs model security and its usage.
Cost/Value: This covers the value and cost of the project and its respective sub-processes.
Bias: Measurement and mitigation strategies for bias are required.
Accountability: Coherent outline of responsibilities.
Audit: Facilitate environment to perform audits continuously and review workflows and systems periodically by in-house department or third parties entities.
Time: The field of AI changes quickly due to innovations and researches, understanding the impact of these strategies and systems.
In addition to a well defined process, other important pillars of an AI Governance program include identifying federated champions across the organization (tech and business), establishing internal and external partnerships to accelerate innovation, and defining usage guidelines for technology tools and frameworks.
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