Article by Meghan Maury: “The Privacy Act of 1974 was designed to give people at least some control over how the federal government uses and shares their personal data. Under the law, agencies must notify the public when they plan to use personal information in new ways – including when they intend to share it with another agency – and give the public an opportunity to weigh in.
At dataindex.us, we track these data-sharing notices on our Take Action page. Recently, a pattern has emerged that you might miss if you’re only looking at one notice at a time.
Since around July of last year, the number and pace of data-sharing agreements between federal agencies and the Department of the Treasury has steadily increased. Most are framed as efforts to reduce “waste, fraud, and abuse” in government programs…
It might be. Cutting waste and fraud could mean taxpayer dollars are used more efficiently, programs run more smoothly, and services improve for the people who rely on them.
I’ve personally benefited from this kind of data sharing. When the Department of Education began pulling tax information directly from the IRS, I no longer had to re-enter everything for my financial aid forms. The process became faster, simpler, and far less error-prone…
The danger comes when automated data matching is used to decide who gets help (and who doesn’t!) without adequate safeguards. When errors happen, the consequences can be devastating.
Imagine a woman named Olivia Johnson. She has a spouse and three children and earns about $40,000 a year. Based on her income and family size, she qualifies for SNAP and other assistance that helps keep food on the table.
Right down the road lives another Olivia Johnson. She earns about $110,000 a year, has a spouse and one child, and doesn’t qualify for any benefits.
When SNAP runs Olivia’s application through a new data-matching system, it accidentally links her to the higher-earning Olivia. Her application is flagged as “fraud,” denied, and she’s barred from reapplying for a year.
This is a fictional example, but false matches like this are not rare. In many settings, a data error just means a messy spreadsheet or a bad statistic. In public benefit programs, it can mean a family goes hungry…(More)”
