  Artificial Intelligence-enabled systems are increasingly being deployed in
real-world safety-critical settings involving human participants. It is vital
to ensure the safety of such systems and stop the evolution of the system with
error before causing harm to human participants. We propose a model-agnostic
approach to detecting unknown errors in such human-centered systems without
requiring any knowledge about the error signatures. Our approach employs
dynamics-induced hybrid recurrent neural networks (DiH-RNN) for constructing
physics-based models from operational data, coupled with conformal inference
for assessing errors in the underlying model caused by violations of physical
laws, thereby facilitating early detection of unknown errors before unsafe
shifts in operational data distribution occur. We evaluate our framework on
multiple real-world safety critical systems and show that our technique
outperforms the existing state-of-the-art in detecting unknown errors.
