Engineering permanence in finite systems

Abstract

The man-machine integration era (MMIE) is marked by sensor ubiquity, whose readings map human beings to finite numbers. These numbers processed by continuously changing, optimizing/learning, finite precision, closed loop, distributed systems are used to drive decisions such as insurance rates, prison sentencing, health care allocations and probation guidelines. Optimization and system parameter tuning is increasingly left to machine learning and applied AI. One challenge we face is thus: Ensuring the indelibility, the permanence, the infinite value of human beings as optimization-resistant invariants in such system environments. In this challenge paper, we propose developing safeguards, specifically working towards a 'deontological imprimatur' architecture embedding resilient AbstractThe man-machine integration era (MMIE) is marked by sensor ubiquity, whose readings map human beings to finite numbers. These numbers-processed by continuously changing, optimizing/learning, finite precision, closed loop, distributed systems-are used to drive decisions such as insurance rates, prison sentencing, health care allocations and probation guidelines. Optimization and system parameter tuning is increasingly left to machine learning and applied AI. One challenge we face is thus: Ensuring the indelibility, the permanence, the infinite value of human beings as optimizationresistant invariants in such system environments. In this challenge paper, we propose developing safeguards, specifically working towards a 'deontological imprimatur' architecture embedding resilient representations of human beings.