Top 4 Applications of AI Governance
This is Part 3 of a series of blogs on IBM AI Governance.
We are in a unique phase right now, where everyone is excited about AI and its potential impact on humanity. Along with bright future prospects, there is also a recognition of significant risks associated with growing AI innovations and adoption.
There is a significant increase in AI rules, regulations, and the need for AI Governance to manage this risk. Organizations are trying to understand this rapidly changing AI landscape and looking for ways to drive responsible use of AI.
The IBM AI Governance solution provides a set of tools and processes to help enterprises drive trustworthy and responsible use of AI. Learn about the three key pillars of AI Governance here.
In this blog, let's look at what is possible with the help of the IBM AI Governance solution. We will focus on top problems that can be solved with IBM AI Governance solutions which are applicable across multiple industries.
Automate AI lifecycle governance at scale with effective collaboration among growing stakeholders
AI governance is about putting the right guardrails in place. AI Lifecycle is a complex process involving a lot of tools, procedures, and people. Enterprises often start small and are able to handle the end-to-end process efficiently but they face problems when they want to scale.
How can you make sure that you can scale your models from 10 to hundreds with standardized governance practices, effective collaboration, and minimum efforts? That’s where automation comes into the picture. IBM can help you with two types of automation to implement AI Governance at scale.
Automation of Model Tracking
Catalog and organize all your machine learning models within your organization at a central place in a model inventory. This gives you the ability to track models across their lifecycle from request to evaluation and production deployment.
Automation of Model Auditing
Automatically document important key characteristics of models across their lifecycle to facilitate enterprise validation.
Generate customizable reports and share them with stakeholders and regulatory authorities to keep business processes auditable and compliant with regulations
If you want, you can choose to automate the build and deploy phases of your lifecycle with IBM Data Science and MLOps capabilities. But even if you are using other ML vendors you can still use the IBM AI Governance solution.
Implement responsible AI in business processes by utilizing fair models
One of the important pillars of responsible AI is fairness and eliminating bias when you are dealing with people's attributes. Whether you are recruiting people as part of the HR team or giving loans within the banking industry this is applicable to everyone. Making sure that your AI systems are not making favorable outcomes for one group over another is critical for your business outcomes and the reputation of the brand.
Fairness evaluation for direct and indirect bias
You can configure fairness monitoring for protected attributes like age, sex, and race to ensure fair outcomes among different groups. Evaluation results are available on a single, easy-to-read dashboard. You can set up a fairness threshold and receive alerts if fairness drops below acceptable value.
If you don’t have typically protected attributes in your data, the monitoring service will look for indirect bias by finding associated values in the training features.
De-bias Techniques
In case of bias is detected, you can choose to de-bias your model actively or passively.
Accelerate business outcomes by adopting transparent and explainable AI
Explainability plays a crucial role in building trust in AI systems which leads to the adoption of AI with confidence. It is important to understand how AI is making decisions that are impacting critical business outcomes. If you belong to a regulated industry like banking then you need to provide an explanation for your decisions example reasons for a declining a loan to an individual.
Global and Local Explainability
IBM AI Governance solution can help you understand factors influencing your model’s decision on a local (specific model transaction level) and global level (overall impact on model outcomes).
You get access to explainability algorithms that are enterprise ready with proprietary enhancements from IBM Research to generate accurate, resilient, and cost-effective explanations.
What if Analysis
As a line of business user, you can do an analysis to understand how different values can change the outcome of the model without writing a single line of code.
AI regulations enforcement with custom and auditable model workflow management
AI adoption is growing and so are regulations around it. The question is are we ready to deal with this frequently changing and growing world of AI regulations and frameworks?
Custom Governance, Risk, and Compliance (GRC) workflows
IBM AI Governance solution is here to help you in this journey. You can create highly customizable GRC workflow as per current regulations and your enterprise requirements. It is easy to add new checkpoints and branches in the existing workflow to enforce new regulations. This allows you to standardize AI processes across your organization with the flexibility to accommodate new changes in the world of AI.
If this is exciting, try a demo or speak to an IBMer here.
Learn more about IBM AI Governance
- Part 1: The Need for AI Governance
- Part 2: 3 Key Areas of AI Governance
