One of the peculiar features of the prosecution of suspected leaker Jeffrey Sterling is that he is charged with a seemingly unlikely count of “mail fraud.”
The government’s contention (in Count Eight of the indictment) is that by leaking information to author James Risen, whose books containing that information were later sent by mail to bookstores, Mr. Sterling engaged in mail fraud.
Mail fraud is no doubt a bad thing to do. But to a surprising extent the opposite is also true. The law is so broadly written that many bad things that a person may do could turn out to be mail fraud.
“The mail and wire fraud statutes essentially outlaw dishonesty,” according to a new survey of the subject prepared by the Congressional Research Service which describes the statutes’ astonishing breadth. (The CRS report does not address the Sterling case.)
“A defendant need not personally have mailed or wired a communication,” the CRS report said; “it is enough that he ’caused’ a mailing or transmission of a wire communication in the sense that the mailing or transmission was the reasonable foreseeable consequence of his intended scheme.”
See “Mail and Wire Fraud: A Brief Overview of Federal Criminal Law,” July 21, 2011. An abridged version of the same report is here (both pdf).
“The mail fraud statute was first enacted in the late nineteenth century in order to prevent city slickers from using the mail to cheat guileless country folks,” the CRS report really says.
These ideas aim to advance the detailed policy solutions needed to foster public trust and implement fairness in the adoption of AI across diverse domains, from healthcare and government benefits to rural access, education, and worker protections.
The evidence is clear: algorithmic pay-setting is established in app-based work, and payroll/timekeeping failures show how software can produce systemic wage harm at scale
While a few states have taken steps to implement decision-making mechanisms for certain AI systems, too many leaders are simply accepting narratives about AI’s purported public benefit at face value – jumping to the “how” of AI implementation before thoroughly vetting potential systems and deciding whether they are appropriate to use at all.
When properly structured — with specific numeric targets, secured financial obligations, independent monitoring, and meaningful enforcement — CBAs transform data center deals into durable community partnerships.