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Datatron 2022 Predictions: Three Things to Watch in AI in 2022

vmblog predictions 2022 

Industry executives and experts share their predictions for 2022.  Read them in this 14th annual series exclusive.

Three Things to Watch in AI in 2022

By Harish Doddi, CEO, Datatron 

Trend #1: The need for localized AI/ML models will significantly increase 

AI and ML models are only as "intelligent" as the data they are fed. When you rely on these models to grow your business, they need to be malleable to the myriad of external factors that will affect your desired outcome. That's why experimenting with localized AI/ML models is becoming more necessary for businesses to have a clear understanding of their demographics. 

When you're implementing AI/ML in your business, typically what happens is that with the first few versions of the models, you can see a lot of change. You're able to quickly move from zero to 60 percent of the way in your AI journey, with just a few tweaks to the algorithm. Going from 60 to 90 percent gets much harder; when you're trying to expand, you must also start thinking more about the differences among your various use cases. Capitalizing on localized models can provide a wider optic and vital insights for businesses to meet their goals and stay at the forefront of competition. 

Trend #2: There will be a much greater focus on model governance. 

We're seeing more business users asking about risk exposure. There's an ongoing push-and-pull between the two sides, the regulatory/compliance component and those focused on increasing the bottom line. The idea of responsible AI - bringing more transparency and visibility - will be a significant focus in the coming year for the business side of the house. Are they just falling into the trap of trying to increase revenue but neglecting to follow guidelines? 

Too often, once AI models leave the lab environment, there's very little visibility with respect to what's happening to them. Questions often go unanswered, such as, "How are these models being deployed?" and "Are these models making the right decisions?" and "Are there compliance issues?" This is where AI model governance can play an important role. It helps restrain and guide machine learning models by bringing accountability and traceability into the mix. As more companies make use of AI, they need to ensure those investments will be worth it - implementing model governance will be increasingly necessary. 

Trend #3: Companies will need to create new job roles to oversee governance and bias 

While bias in algorithms is related to the aforementioned model governance issue, we expect it to be a big enough issue in the coming year that it almost stands alone.  

Ensuring machine learning models don't make bad decisions or start developing biases towards certain sets of data has been no easy task for enterprises.

To grapple with this, we expect to see more companies establishing positions such as chief AI officer, chief AI compliance officer and other emerging titles whose entire job is to foresee the potential model failures ahead. In the next few years, as business users see the benefits of AI/ML, it's just a matter of time before they also start seeing problems - and organizations need to get ahead of that. They need to take a more forward-thinking approach to ensure they are rooting out these biases before they become problems. 



Harish Doddi 

Harish Doddi specialized in Systems and Databases field where he started his career in Oracle. He later worked at Twitter to work on open source technologies. He then managed the Snapchat stories product from scratch and the pricing team at Lyft. He has finished his undergrad in Computer Science from International Institute of Information Technology (IIIT-Hyderabad) and then graduated with Masters in Computer Science degree from Stanford University.

Published Monday, December 20, 2021 7:31 AM by David Marshall
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