This post is part of a series designed to help AI-interested regulated industry leaders overcome challenges to successful AI design. For more information, download Top Challenges to Designing AI in Regulated Industries [And How to Overcome Them].
Other posts in this series include:
When it comes time to justify the resources spent on AI, how can a regulated industry leader be sure their initiatives are on target and providing value?
Key performance indicators, or KPIs, can be used to gauge the performance and return on investment (ROI) of a number of business initiatives. Similar to other projects, the KPIs your organization measures for AI should be based on your goals and overall automation strategy.
Read on to discover the importance of setting the right KPIs for your organization’s artificial intelligence strategy.
Reliable KPIs are measurable and transferable—meaning that they can be directly recorded and then used indirectly to inform other metrics. In healthcare, for example, the speed and precision of a model’s clinical diagnosis could be measured as direct metrics, which could then influence an indirect metric like patient satisfaction.
KPIs are frequently set to measure factors like data quality or security. Some may also track a hallmark of Trusted AI, like privacy (with a metric like location entropy) or fairness (with proportional parity).
Other commonly used KPIs for AI include:
While each of these options can be useful, organizations should narrow their KPIs to one or two that measure return on investment (ROI).
The KPIs you select should depend on your AI strategy and overall goals. Here’s how choosing the right KPIs can benefit your organization.
KPIs can be used to establish acceptable margins of error in your strategy, which in turn gives you the opportunity to create an action plan for outputs that fall outside these margins. Think: How will you respond to an outlying data point in a protected class, like age, gender, or ethnicity?
As more and more companies explore the potential of AI, KPIs can be enormously helpful in generating support and future buy-in. Consider how a KPI that measures time or money saved could showcase the value of an AI model to a CFO.
A KPI that continuously falls short of its goal could indicate an area of the model that needs to be re-examined.
For example, If your KPI related to data quality is consistently lower than expected, it may mean that you need to evaluate your model’s training process more closely.
In regulated industries, establishing reliable KPIs for AI is a critical component of organizational growth. When used correctly, KPIs can justify important investments, strengthen the performance of your team, and help you become a more ethical employer of technology.
Interested in learning more about the AI design challenges faced by regulated industry leaders? Globally renowned AI Strategist, Cal Al-Dhubaib, shares some of the top challenges he’s seen throughout his years as a data scientist—and how you can avoid them—in the complimentary resource, Top Challenges to Designing AI Solutions in Regulated Industries [And How To Overcome Them].
Contributor: Merilys Huhn is a Data Science Consultant at Pandata.