Schleiß, Philipp

Filters

2 1
2
2
Reset filters

Settings
Now showing 1 - 2 of 2
  • Publication
    AI for Safety: How to use Explainable Machine Learning Approaches for Safety Analyses
    ( 2023)
    Kurzidem, Iwo
    ;
    Burton, Simon
    ;
    Schleiß, Philipp
    Current research in machine learning (ML) and safety focuses on safety assurance of ML. We, however, show how to interpret results of explainable ML approaches for safety. We investigate how individual evaluation of data clusters in specific explainable, outside-model estimators can be analyzed to identify insufficiencies at different levels, such as (1) input feature, (2) data or (3) the ML model itself. Additionally, we link our finding to required artifacts of safety within the automotive domain, such as unknown unknowns from ISO 21448 or equivalence class as mentioned in ISO/TR 4804. In our case study we analyze and evaluate the results from an explainable, outside-model estimator (i.e., white-box model) by performance evaluation, decision tree visualization, data distribution and input feature correlation. As explainability is key for safety analyses, the utilized model is a random forest, with extensions via boosting and multi-output regression. The model training is based on an introspective data set, optimized for reliable safety estimation. Our results show that technical limitations can be identified via homogeneous data clusters and assigned to a corresponding equivalence class. For unknown unknowns, each level of insufficiency (input, data and model) must be analyzed separately and systematically narrowed down by process of elimination. In our case study we identify "Fog density" as an unknown unknown input feature for the introspective model.
  • Publication
    Safety Assurance of Machine Learning for Chassis Control Functions
    ( 2021)
    Burton, Simon
    ;
    Kurzidem, Iwo
    ;
    Schwaiger, Adrian
    ;
    Schleiß, Philipp
    ;
    Unterreiner, Michael
    ;
    Graeber, Torben
    ;
    Becker, Philipp
    This paper describes the application of machine learning techniques and an associated assurance case for a safety-relevant chassis control system. The method applied during the assurance process is described including the sources of evidence and deviations from previous ISO 26262 based approaches. The paper highlights how the choice of machine learning approach supports the assurance case, especially regarding the inherent explainability of the algorithm and its robustness to minor input changes. In addition, the challenges that arise if applying more complex machine learning technique, for example in the domain of automated driving, are also discussed. The main contribution of the paper is the demonstration of an assurance approach for machine learning for a comparatively simple function. This allowed the authors to develop a convincing assurance case, whilst identifying pragmatic considerations in the application of machine learning for safety-relevant functions.