AI (Artificial Intelligence) Governance: How To Get It Right
AI (Artificial Intelligence) governance is about evaluating and monitoring algorithms for effectiveness, risk, bias and ROI (Return On Investment). But there is a problem: Often not enough attention is paid to this part of the AI process.
“AI projects are rarely coordinated across a company and data science teams are often isolated from application development,” said Mike Beckley, who is the CTO of Appian. “And now regulators are starting to ask questions businesses don’t now how to answer.”
Keep in mind that AI introduces unique problems. Training data is often flawed, such as with errors, duplications and even bias. Then there is the issue with model drift. This is when the AI degrades over time because the algorithms and data do not adequately reflect the changes in the real world.
The result is that a company may make bad decisions or miss revenue opportunities. Even worse, there is the potential for the AI to be unfair or discriminatory.
OK then, what about software tools to help with these problems? Can AI governance be automated? Well, this is an area of technology that is in the nascent stages.
This means that AI governance requires a hands-on approach. “It’s about managing processes and people to get the best results,” said Kenn So, who is a venture capitalist at Shasta Ventures.
So what are some best practices to consider? What can be done to put together a good framework for AI governance? Interestingly enough, if you already have a data policy in place, then you have a head start.
“The relationship between data and AI is so close,” said Wilson Pang, who is the Chief Technology Officer of Appen. “What data do you have? Where is it coming from? How is the data being altered? By whom?”
But of course, there are other things to think about. First of all, it’s important to note that data scientists have different approaches and skillsets than application developers. This can easily lead to a breakdown in communications. In other words, there needs to be clear-cut requirements and principles.
Next, you should put together an AI governance plan. “You need this before you send a machine learning algorithm into the wild, whether it be software for image analysis, a recommendation engine, or a voice-enable commerce bot,” said Rachel Roumeliotis, who is the Vice President of Content Strategy forO’Reilly. “This is important not just for extra regulated industries like finance and banking and healthcare, but just makes sense if you are making decisions based on an algorithm’s output that will affect your company and clients.
