Governance in AI can often be seen as an impediment to faster innovation because of the need to comply with regulations and adhere to ethical considerations – slowing down time to market.
But Priya Krishnan, IBM’s director of governance and data science product, said it is better to think of it like the safety controls in a car.
“You have the brakes in the car, you have your seat belts – all of these are meant for you to drive safely faster to your destination,” she said at the AI Summit Austin. “It’s not meant to slow you down. Think of AI the same way.”
Robust governance AI has to be comprehensive and consistent as metrics change, Krishnan said. It also has to be end to end, open to complement an organization’s existing tools and offer automated capture of metadata.
The trifecta of people, process and technology is critical in developing a good AI governance solution, but “more often than not that’s where we start,” Krishnan said.
Identify stakeholders, specify the business use case and be clear about what goals to accomplish. “Finally, pick the technology that will help you scale as you move through this landscape especially with changing regulations,” she said.
Steps To Robust AI Governance
AI governance also means bringing together different stakeholders beyond the data science and IT teams. For example, poor governance can affect the company’s brand, so the chief marketing officer would want to get involved. Not complying with regulations introduces financial risk, so the CFO would likely be interested as well.
“It’s not just a team of data scientists creating these models but many stakeholders are involved in this process,” Krishnan said.
AI governance also means managing risk to ensure responsible use of AI across a company’s many business controls and standards, as well as adhering to expanding regulations around the globe, Krishnan added.