Oversight of Unsafe Systems via Dynamic Safety Envelopes
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This paper reviews the reasons that Human-in-the-Loop is both critical for preventing widely-understood failure modes for machine learning, and not a practical solution. Following this, we review two current heuristic methods for addressing this. The first is provable safety envelopes, which are possible only when the dynamics of the system are fully known, but can be useful safety guarantees when optimal behavior is based on machine learning with poorly-understood safety characteristics. The second is the simpler circuit breaker model, which can forestall or prevent catastrophic outcomes by stopping the system, without any specific model of the system. This paper proposes using heuristic, dynamic safety envelopes, which are a plausible halfway point between these approaches that allows human oversight without some of the more difficult problems faced by Human-in-the-Loop systems. Finally, the paper concludes with how this approach can be used for governance of systems where otherwise unsafe systems are deployed.
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