Decision Rights

TL;DR for operators Dividing a high-stakes decision among specialist agents does not specify who may see which evidence, who owns each subdecision, or who can revise it. HiMA-MDD1 turns those choices into explicit system rules for PHQ-8 assessment from completed multimodal clinical interviews. The strongest architectural signal is not “more agents perform better.” Before global verification, four specialists produce the lowest total-score error, while two specialists produce the highest screening kappa and Macro-F1. Removing cross-factor audit and targeted revision causes the largest screening-performance decline among the paper’s three component ablations. Giving four specialists bounded item-specific evidence also beats giving them a capped shared evidence pool on every reported pre-verification metric, although that experiment changes both evidence composition and context length. ...

TL;DR for operators A high-stakes AI system can fail in two very different ways: it can act on something that is not true, or it can fail to act when danger is real. Those errors need not have comparable consequences. Martino Maggetti’s Reciprocal Trust and Distrust in Artificial Intelligence Systems: The Hard Problem of Regulation1 argues that this asymmetry should influence who receives final decision authority. In nuclear launch and strategic-warning settings, where a false positive could trigger catastrophic action, the paper favors protected human authority, independent corroboration, and explicit uncertainty. In reactor, chemical-process, and flight-control settings, where failing to intervene can be catastrophic, it allows for bounded AI authority through mechanisms such as non-overridable shutdown logic. ...