Understanding Problems in AI Governance
AI governance will have to be one of the most important pieces of the puzzle as machine learning and artificial intelligence become more integral parts of our society. You can say there is an AI arms race happening right now. However, not every company currently has the tools necessary to perform well when it comes to building AI models.
They might cut corners and do other things to catch up. AI governance is crucial in this case because model management can cause a lot of problems. Model risk can have a massive downside for many companies adopting these technologies. Regulation is inevitable, so companies need to be ahead of the curve when it comes to the best practices governance can provide. The other problem is that model validation will become extremely valuable with time, along with these other factors.
The Current Industry Situation
There are currently many problems when it comes to model risk management. We never see most of the bad things that happen with AI inside an organization, but this will become more visible as model management moves to a more public place. The various risks have to do with data retention, fairness, and transparency. It is going to affect companies of all sizes that utilize these technologies. The public is increasingly becoming more aware, leading to more calls for regulation.
Many people will be less amenable to using these solutions because they will worry that it will lead to less favorable results for themselves. For example, people will want AI governance when they learn that a certain model discriminates against them. They might even want that technology to be banned altogether, leading to big problems for the entire industry. The most apparent cases are in banking when handing out credit cards and loans.
Model Risk Management
Companies will have to get good at model risk management and model validation. It will be critical in industries that rely on customer goodwill and favorability among the general public. If you are known as a company with a model that discriminates, you might find yourself under the watchful eye of your local regulator. Understanding model risk is crucial, but that will pose a lot of problems for companies that might not have the resources or know-how to implement successful solutions.
The fact of the matter is that you will never eliminate risk completely. That is why model risk management is about minimizing it with as few resources as possible. The challenge comes with finding that sweet spot that allows you to get the most bang for your buck.
Successful and Responsible AI
The crucial component of model risk management is using AI governance to alleviate those problems before they grow bigger. If you can do that, you can successfully accomplish what many companies can only dream of. Good AI governance allows you to stop the problems before they get bigger.
Your organization needs to have strict policies with a lot of documentation when it comes to implementing AI initiatives. Model validation should include aspects of good AI governance and fairness. You need to have a mindset where you are always thinking about what could go wrong to hurt people with every AI model you build. Model management should include risk at its core.