Quantifying Assurance in Learning-enabled Systems

Dependability assurance of systems embedding machine learning(ML) components---so called learning-enabled systems (LESs)---is a key step for their use in safety-critical applications. In emerging standardization and guidance efforts, there is a growing consensus in the value of using assurance cases for that purpose. This paper develops a quantitative notion of assurance that an LES is dependable, as a core component of its assurance case, also extending our prior work that applied to ML components. Specifically, we characterize LES assurance in the form of assurance measures: a probabilistic quantification of confidence that an LES possesses system-level properties associated with functional capabilities and dependability attributes. We illustrate the utility of assurance measures by application to a real world autonomous aviation system, also describing their role both in i) guiding high-level, runtime risk mitigation decisions and ii) as a core component of the associated dynamic assurance case.Comment: Author's pre-print version of manuscript accepted for publication in the Proceedings of the 39th International Conference in Computer Safety, Reliability, and Security (SAFECOMP 2020

Towards Quantification of Assurance for Learning-enabled Components

Perception, localization, planning, and control, high-level functions often organized in a so-called pipeline, are amongst the core building blocks of modern autonomous (ground, air, and underwater) vehicle architectures. These functions are increasingly being implemented using learning-enabled components (LECs), i.e., (software) components leveraging knowledge acquisition and learning processes such as deep learning. Providing quantified component-level assurance as part of a wider (dynamic) assurance case can be useful in supporting both pre-operational approval of LECs (e.g., by regulators), and runtime hazard mitigation, e.g., using assurance-based failover configurations. This paper develops a notion of assurance for LECs based on i) identifying the relevant dependability attributes, and ii) quantifying those attributes and the associated uncertainty, using probabilistic techniques. We give a practical grounding for our work using an example from the aviation domain: an autonomous taxiing capability for an unmanned aircraft system (UAS), focusing on the application of LECs as sensors in the perception function. We identify the applicable quantitative measures of assurance, and characterize the associated uncertainty using a non-parametric Bayesian approach, namely Gaussian process regression. We additionally discuss the relevance and contribution of LEC assurance to system-level assurance, the generalizability of our approach, and the associated challenges.Comment: 8 pp, 4 figures, Appears in the proceedings of EDCC 201

Innovation dynamics and the role of infrastructure

This report shows how the role of the infrastructure – standards, measurement, accreditation, design and intellectual property – can be integrated into a quantitative model of the innovation system and used to help explain levels and changes in labour productivity and growth in turnover and employment. The summary focuses on the new results from the project, set out in more detail in Sections 5 and 6. The first two sections of the report provide contextual material on the UK innovation system, the nature and content of the infrastructure knowledge and the institutions that provide it. Mixed modes of innovation, the typology of innovation practices developed and applied here, is constituted of six mixed modes, derived from many variables taken from the UK Innovation Survey. These are: Investing in intangibles Technology with IP innovating Using codified knowledge Wider (managerial) innovating Market-led innovating External process modernising. The composition of the innovation modes, and the approach used to compute them, is set out in more detail in Section 4. Modes can be thought of as the underlying process of innovation, a bundle of activities undertaken jointly by firms, and whose working out generates well known indicators such as new product innovations, R&D spending and accessing external information, that are the partial indicators gathered from the innovation survey itself