AI models have surely been the engine for the ongoing wave of innovation. However, deploying efficient AI models is also necessary for improved performance and outcomes. Additionally, it is important to ensure that AI models are well governed while considering the development and implementation of responsible, ethical, and compliant AI systems.
Though over 78% of businesses across the globe use AI to streamline at least one of their processes, very few of them have a proper model governance framework. As a result, many organizations fail to deploy AI systems successfully and maintain their ethical concerns.
In this blog, we’ll understand model governance, its importance, and the major components of a model governance framework. Let’s dive in…
What is Model Governance?
Model governance refers to strategies, guidelines, and set rules to control, validate, develop, and deploy AI models while ensuring accountability and ethical components. The governance parameters enable organizations to maintain transparency in how their AI models work and operate. It also enables thorough monitoring and observability during successful AI model deployment.
A model governance framework aims to ensure that AI models operate to fulfill their primary goal while remaining compliant and adhering to ethical guidelines. Such an approach establishes trust and boosts the effectiveness of the AI systems. Furthermore, it helps manage and eliminate risks associated with AI models, such as bias, data poisoning, privacy, and others.
Importance of Having a Robust Model Governance Framework:
Governance is crucial for any systems or strategies implemented within an organization. AI integration doubtlessly possesses the potential to transform businesses; however, inefficient governance can lead to diverse conclusions. It can further impact the trust and reliability of a business. Let us discuss a few elements to understand the significance of model governance strategies-
- Shortcomings in AI models: AI models are trained on vast datasets. It is highly possible that the data used is biased and propaganda driven. As a result, AI models can generate biased outcomes and reflect multiple errors. A governance strategy helps maintain data standards while empowering models to make fewer errors.
- Ethical Concerns: AI bias is also an ethical concern for businesses as well as their customers. It can lead to regulatory violations and divert models from fulfilling their primary goals.
- Operational Efficiency: Trustworthy AI models can be implemented and used in a risk-free manner. Appropriate governance ensures model efficiency and consistency, which further helps in attaining operational excellence.
- Boost Trust and Transparency: A model governance framework helps maintain transparency when using an AI model and meet organizational goals. This also helps establish trust and use tools like AI more efficiently.
Major Components of a Model Governance Framework:
The components of a model governance framework ensure that models are appropriately regulated and establish accountability. Governance is a continuous process that starts from the stage of model development and has to be sustained post-deployment. Here are the major components to look after-
Model Development and Documentation:
Development is a primary stage where governance has to be sustained. It includes governing the key objectives, training data, data sources, and model inputs. Alongside that, model documentation is also a crucial stage as it works as a blueprint of the model objectives, training data, and data sources, which lay the foundation of model development.
Ownership and Roles:
Effective governance can be established through team effort, with ownership, roles, responsibilities, and accountability distributed equally. Hence, data scientists, compliance teams, risk officers, and decision-makers must come together to form a team and govern the AI model across the development and deployment lifecycle.
Model Validation and Testing:
It is important to eliminate the risks of an AI model through comprehensive testing and validation procedures. Here, validation teams play a key role. They run tests and validation activities of the effectiveness, pre-deployment bias, and fairness of the models.
Model Deployment and Monitoring:
Governance continues after the AI model is deployed. Data and AI teams consistently monitor the model’s performance after deployment. They observe and track its accuracy while detecting errors to improve performance.
Risk and Compliance Control:
After deployment, it is necessary to assess the alignment of the model with the security, privacy, and compliance protocols. This helps in adhering to the ethical guidelines, eliminating the possibilities of violation.
Challenges of Adopting a Robust Model Governance Framework:
Though governance is an unignorable aspect of AI and ML model development, several challenges can arise. Here are the major difficulties-
Complexity of Models: Organizations generally develop and deploy complex AI models to execute their complicated tasks. Many models also depend on third-party facilities. Managing and monitoring such models often becomes challenging.
Explainability of Models: Due to the complexity of models, AI experts often struggle to interpret them. Under such circumstances, governance becomes tough.
Lack of Resources: Model governance frameworks need a team of data scientists, AI experts, and risk management professionals. Organizations mostly fail to hire the right talent for the same reason, resulting in a lack of resources.
Building Safer AI Models!
As long as AI and ML models help organizations improve their processes, governance strategies will remain crucial. Whether in the finance, retail, or healthcare sector, adopting an efficient governance framework is equally important. Governance not only allows businesses to monitor models in a resilient way but also offers opportunities for improvisation.
Check out our informative blogs for in-depth takeaways on AI, ML, and other emerging innovations!
F&Qs:
Q1: What are the 4 P’s of governance?
Answer: People, process, performance, and purpose are the four P’s of governance.
Q2: What are the 4 pillars of data governance framework?
Answer: Data quality, data stewardship, data protection and compliance, and data management are the four pillars of a data governance framework.
Related Read:
Data Governance Guiding Principles Explained
The Importance of Data Governance Maturity Model
Data Governance Maturity Model Framework Managing, Using, Innovating Data Assets