Watch more: TechReg With Catherine Tucker and Rozhina Ghanavi of MIT Sloan
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An artificial intelligence agent researching a medical condition can learn something about an entire family from a conversation with just one member.
Catherine Tucker, professor of management at MIT Sloan, used that example to describe a weakness in privacy rules built around individual disclosure. A person asking about macular degeneration might volunteer a family history to obtain a useful answer, revealing information about relatives who never agreed to share it.
“Do they have the same degree of economic security, which leaves me to reveal this very personal and potentially problematic information? No,” Tucker told Competition Policy International (CPI), a PYMNTS company, in an interview.
The hypothetical illustrates an economic issue that runs through her research, conducted in collaboration with Rozhina Ghanavi, a PhD candidate at MIT Sloan. The person receiving the benefit of disclosure does not necessarily bear all its costs.
Agentic AI delegates more of these choices to software. An agent given access to email, calendars, customer records or payment systems makes decisions about retrieving and using information while completing a task. Privacy arrangements premised on a person approving each disclosure become difficult to apply when the person has delegated that judgment.
” Ending a connection, limiting its duration and deciding what information an agent retains require answers beyond permission to process a particular record.
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