Autonomous systems are increasingly deployed across critical domains, yet research on trustworthy autonomous systems (TAS) appears to lack an agreed-upon, operationalizable taxonomy for trustworthiness characteristics. While trustworthy artificial intelligence (TAI) frameworks offer measurable characteristics with more established metrics, TAS literature tends to be fragmented with high-level models that can be difficult to apply in practice. This paper investigates the extent of alignment in TAS trustworthiness research and identifies potential sources of misalignment, including differences in system definitions, autonomy properties, stakeholder perspectives, and domain requirements. Based on this analysis, we synthesize a foundational two-pillar core of trustworthiness, the system’s ability to dependably do what it is intended to do and the extent to which stakeholders can assess and justify confidence in this behavior. We propose an initial operationalization of this core using characteristics adapted from established TAI frameworks for which metrics and evaluation methods exist, and outline how it can be applied to specific application contexts. An example shows how stakeholder analysis guides characteristic adaptation.

Deep learning methods have gained popularity in high energy physics for fast modeling of particle showers in detectors. Detailed simulation frameworks such as the gold standard \textsc{Geant4} are computationally intensive, and current deep generative architectures work on discretized, lower resolution versions of the detailed simulation. The development of models that work at higher spatial resolutions is currently hindered by the complexity of the full simulation data, and by the lack of simpler, more interpretable benchmarks. Our contribution is \textsc{SUPA}, the SUrrogate PArticle propagation simulator, an algorithm and software package for generating data by simulating simplified particle propagation, scattering and shower development in matter. The generation is extremely fast and easy to use compared to \textsc{Geant4}, but still exhibits the key characteristics and challenges of the detailed simulation. The proposed simulator generates thousands of particle showers per second on a desktop machine, a speed up of up to 6 orders of magnitudes over \textsc{Geant4}, and stores detailed geometric information about the shower propagation. \textsc{\textsc{SUPA}} provides much greater flexibility for setting initial conditions and defining multiple benchmarks for the development of models. Moreover, interpreting particle showers as point clouds creates a connection to geometric machine learning and provides challenging and fundamentally new datasets for the field.

The two key characteristics of a normalizing flow is that it is invertible (in particular, dimension preserving) and that it monitors the amount by which it changes the likelihood of data points as samples are propagated along the network. Recently, multiple generalizations of normalizing flows have been introduced that relax these two conditions \citep{nielsen2020survae,huang2020augmented}. On the other hand, neural networks only perform a forward pass on the input, there is neither a notion of an inverse of a neural network nor is there one of its likelihood contribution. In this paper we argue that certain neural network architectures can be enriched with a stochastic inverse pass and that their likelihood contribution can be monitored in a way that they fall under the generalized notion of a normalizing flow mentioned above. We term this enrichment \emph{flowification}. We prove that neural networks only containing linear and convolutional layers and invertible activations such as LeakyReLU can be flowified and evaluate them in the generative setting on image datasets.