Artificial Intelligence (AI) at its core is a mathematical equation. Al takes a large amount of data, analyzes it, and produces predictions. Ensuring these predictions are accurate is where a governance framework can help. Data governance addresses concerns surrounding AI by increasing transparency and accountability by creating policies and processes. Overall, strong governance is necessary to maximize the benefits of AI while minimizing risks. Some key aspects include:
Clarify ownership and accountability
Clearly defining ownership and accountability of AI development is essential. By clearly outlining employee’s roles and responsibilities and ensuring the team tasked with developing and deploying AI models has the necessary data, tools, and follows best practices.
Establish cross-functional teams
Creating cross-functional teams that include individuals with expertise in various areas is critical in ensuring AI models are accurate. Collaborating with different departments, such as legal, compliance, and security, ensures AI models align with institutional goals and follow regulations.
Implement data tracking and issue resolution
Data tracking allows institutions to catch any issues during the development process, monitor performance, and make informed changes when necessary post-deployment. This real-time monitoring approach helps identify and resolve development and potential quality issues, such as bias or non-compliance, more efficiently.