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Judge poised to approve Michigan unemployment glitch settlement

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The state of Michigan is close to settling a long-lingering lawsuit over the Unemployment Insurance Agency’s false accusations of fraud leveled against thousands of people.

The class-action lawsuit dates back to then-Governor Rick Snyder’s administration and the switch to an automated computer system that falsely flagged people as having fraudulently been paid benefits. The state’s collection efforts included seizing tax refunds and garnishing wages.

The state’s set aside $220 million to pay the settlements.

Michigan Court of Claims Judge Douglas Shapiro said he’s ready to finalize the deal.

“Well, I do anticipate signing everything at this point,” he said during an online hearing with attorneys for all sides.

Plaintiffs’ attorney Michael Pitt says the settlement will cover a wide range of damages linked to the Unemployment Insurance Agency’s failure.

“Bankruptcy, eviction, foreclosure, repossession of personal property, credit rating decline, mental health treatment, mental health impact, loan declinations, job loss, divorce, impairment of family relationships,” he said.

About 8,200 people could be affected by the agreement. These are people who were ordered to repay the state due to a computer glitch. The problem dates back to 2013.

Attorney Jennifer Lord said the situation was so confusing that, while some people were forced into bankruptcy, in other cases, struggling families were not allowed that avenue to deal with mounting debt.

“Once we really dug down, we learned what was happening is that the agency was actually going in and filing adverse proceedings in 1,100 bankruptcies,” she said.

The agreement does not require plaintiffs to prove that the state’s misfeasance caused the damage, but to make a reasonable case the error contributed to their problems.

Michigan Court of Claims Judge Douglas Shapiro is expected to sign the approval order on January 19.

Copyright 2023 Michigan Radio. To see more, visit Michigan Radio.

Rick Pluta | MPRN