Looking for a promotion or a new job? Secret statistical programs could doom or aid your chances
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If you’ve been checking LinkedIn to search for a job, your employer may know.
That’s the conclusion of a new book called The Reputation Economy: How to Optimize Your Digital Footprint in a World Where Your Reputation Is Your Most Valuable Asset. The New York Bits blog has a story.
If a recruiter notices your browsing history, you might get some cold calls about available jobs, the book says. But if your company notes your LinkedIn browsing with a computer analytics program, “look forward to either a conversation about what it would take to keep you—or a swift kick toward the door,” the book says.
Companies use computer scoring programs to rate their own workers and to assess the resumés of job candidates, according to The Reputation Economy and another book, The Black Box Society: The Secret Algorithms That Control Money and Information.
In The Black Box Society, University of Maryland law professor Frank Pasquale says the average job-seeker or employee is unaware of the criteria used in data mining programs.
“Secret statistical methods for picking and assessing employees seem to promise a competitive edge,” Pasquale writes. “Whether these methods deliver or not is unclear, and they feel ‘creepy’ to many workers, who fear having a critical aspect of their lives left to mysterious and unaccountable computer programs.”
Pasquale argued for new laws to address transparency in a prior New York Times column.
Doctors and hospitals, which are required to give patients access to their records, should also have to reveal health profiles compiled and traded by data brokers, Pasquale says. And the Fair Credit Reporting Act, which gives consumers the right to access and correct information with credit reporting agencies, should be extended to cover data brokers, he says.
Pasquale also says data brokers should have to register with the Federal Trade Commission, and when requested, should have to tell consumers when they have been placed on lists with sensitive data.
