AI can accomplish incredible things when leaders focus on these 3 areas

The Covid-19 pandemic may have created economic uncertainty, but it’s a testament to the incredible excitement surrounding AI innovation that investments in the space largely weathered the storm: Just 7 percent of investments decreased, and 16 percent were temporarily suspended in 2020, while 47 percent remained unchanged and 30 percent were set to increase.

Over the past few years, AI products have gained momentum for many reasons, but two, in particular, stand out. First, the data being generated continues to exponentially increase, even as it takes on new forms and emerges from a wider geographical footprint. More data and, critically, more diverse data is better for teaching an AI model to avoid bias and accomplish all kinds of goals.

The other factor contributing to AI’s success is the sheer number of tools available to CIOs and CTOs to assist with implementations, from data collection and enrichment tools to annotation search engines, machine learning model developers, and ML model testers.

Just a few years ago, technology leaders hoping to take on AI development needed to start by building a platform to label or annotate training data — a process that might take almost 80 percent of the total development time. That’s a tough pill to swallow. Now, though, sophisticated platforms are readily available to streamline the development process significantly.

Despite the accelerating pace of AI development, real challenges persist. Modeling bias is one of the biggest barriers to AI adoption, and it’s a complex problem to diagnose because bias can occur from multiple angles simultaneously.

For example, when U.S. states including Florida sought to predict rates of recidivism after prisoners were released, the COMPAS AI tool predicted twice as many false positives for Black prisoners. That’s a really serious issue. While some of the blame surely falls on the AI model, the team also failed to account for the presence of bias in many other aspects of the prison system.

Data itself can also be an obstacle. Although it’s being generated at an incredible rate, that doesn’t mean acquiring it is easy — far from it. Many organizations hesitate to share data because of valid concerns about privacy and malpractice, and sharing data internationally requires adhering to many complex protocols. The creation of a universal data regulation could someday facilitate data transfer while protecting consumer privacy, but such a development is not likely anytime soon.

In the meantime, AI leaders focus on the following three priorities when developing the next generation of groundbreaking AI products:

Developing AI solutions can be expensive, so it’s critical to take steps throughout the process to protect your investment. It’s not uncommon to spend huge sums and commit untold hours to develop an AI tool only to have it give erroneous results and show obvious bias. The most important measure you can take to avoid this outcome is to begin by clearly defining goals that have the user’s best interest in mind.

With clear goals established, you can create a road map to achieve them — but don’t be afraid to look for qualitative as well as quantitative feedback along the journey.