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1-In-10 Workers To Try And Trick Artificial Intelligence Systems

AI-Driven systems have seen a major rise in use in the wake of the Covid-19 pandemic

More than one-in-ten workers will try to trick artificial intelligence (AI) systems used to measure employee behavior and productivity by 2023, according to a prediction by Gartner on Monday. Such systems have seen a major rise in use in the wake of the Covid-19 pandemic.

“Just as we’ve seen with every technology aimed at restricting its users, workers will quickly discover the gaps in AI-based surveillance strategies,” Whit Andrews, Distinguished Research Vice President at Gartner, said in a statement.

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“They may do so for a variety of reasons, such as in the interest of lower workloads, better pay or simply spite. Some may even see tricking AI-based monitoring tools as more of a game to be won than disrespecting a metric that management has a right to know.”

Artificial intelligence system
Many employers use productivity monitoring systems. Pixabay

Organizations are using AI-enabled systems to analyze worker behavior in the same way that Artificial Intelligence systems are used to understand shoppers, customers, and members of the public. These tools provide basic activity logging with alerts, or in more sophisticated versions, can attempt to detect positive actions or misbehavior through multivariable analysis. Many employers use productivity monitoring systems despite a high percentage of workers finding such tools unappealing.

ALSO READ: AI-Driven Sensor Can Monitor Brain Serotonin Levels

Even prior to the pandemic, Gartner’s research showed that workers feared new technologies used to track and monitor work habits. As these tools become more prevalent, organizations will increasingly face workers who seek to evade and overwhelm them, Gartner said. Workers may seek out gaps where metrics do not capture activity, accountability is unclear, or the AI can be fooled by generating false or confusing data.

“IT leaders who are considering deploying AI-enabled productivity monitoring tools should take a close look at the data sources, user experience design, and the initial use case intended for these tools before investing,” said Andrews. (IANS)

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