Gaining Control Over AI, Machine Learning Posted on : Sep 22 - 2017

As artificial intelligence and machine learning technologies make their way into advanced data management platforms, the emphasis for developers and data scientists is broadening to include not just deployment but “control” of the data accessed by these automation tools.

Along with the goal of completing often-tedious data science tasks in minutes and even, some claim, seconds, new enterprise tools also emphasizing greater visibility and control of the development processes underpinning AI and machine learning applications.

One such data management platform released this week by analytics startup Immuta of College Park, Md., seeks to ride the wave of AI and cognitive systems algorithms increasingly used by business to augment decision-making. The company argues that relying on these algorithms make companies more susceptible to risks ranging from critical errors to fraud.

To gain control of algorithm-driven business models, the company argues: “Organizations require greater control over the data being used by machine learning and AI models.”

 “As organizations rely more heavily on algorithms to make critical business decisions, they will be required to demonstrate exactly how and why these decisions are being made, especially when it’s concerning important consumer choices, like credit, loans, healthcare and more,” explained Immuta CEO Matthew Carroll. “The key to explaining decisions made by machine learning and AI models starts by gaining a deeper understanding of the underlying data being used by the systems.”

Immuta’s data management platform is designed to provide greater control of the data fed into algorithms, speeding deployment as well as increasing visibility into how automation tools are functioning. View More