Enhancing Covid-19 Decision-Making by Creating an Assurance Case for Simulation Models

Simulation models have been informing the COVID-19 policy-making process. These models, therefore, have significant influence on risk of societal harms. But how clearly are the underlying modelling assumptions and limitations communicated so that decision-makers can readily understand them? When making claims about risk in safety-critical systems, it is common practice to produce an assurance case, which is a structured argument supported by evidence with the aim to assess how confident we should be in our risk-based decisions. We argue that any COVID-19 simulation model that is used to guide critical policy decisions would benefit from being supported with such a case to explain how, and to what extent, the evidence from the simulation can be relied on to substantiate policy conclusions. This would enable a critical review of the implicit assumptions and inherent uncertainty in modelling, and would give the overall decision-making process greater transparency and accountability.Comment: 6 pages and 2 figure

Simple Interest

Following the deadline for payment protection insurance claims, which passed last year, David Massey and Amy Lawton explain that tax problems can arise when compensation payments are made. Key points ● Do not assume that the taxable element of a client’s PPI compensation payment will be trivial. ● Few individuals will have paid the correct tax. ● The payment received may not be the same as that shown in the documents. ● The time limit for repayment claims for 2016-17 is 5 April 2021

Enhancing COVID-19 decision making by creating an assurance case for epidemiological models

When the UK government was first confronted with the very real threat of a COVID-19 pandemic, policy-makers turned quickly, and initially almost exclusively, to scientific data provided by epidemiological models. These models have had a direct and significant influence on the policies and decisions, such as social distancing and closure of schools, which aim to reduce the risk posed by COVID-19 to public health.1 The models suggested that depending on the strategies chosen, the number of deaths could vary by hundreds of thousands. From a safety engineering perspective, it is clear that the data generated by epidemiological models are safety critical, and that, therefore, the models themselves should be regarded as safety-critical systems

Volunteering - putting your skills into practice

Amy Lawton and David Massey explain how a team of students and tax professional volunteers ran a tax clinic to provide community support

Help Yourself

Amy Lawton and David Massey discuss ways that taxpayers can help improve HMRC’s guidance

Mapping Motivations: self-determination theory and clinical tax education

The North West Tax Clinic is the first, student-led tax clinic in the UK. During its pilot between January and March 2020, the clinic experienced highs and lows in terms of the number of clients accessing the service. This paper, in co-authorship with one of the student volunteers, serves to present a co-reflection that maps out motivation onto the timeline of the clinic pilot. To do so, this paper draws on Self Determination Theory and student surveys to explore how the North West Tax Clinic encouraged autonomy, relatedness and competence. It is argued that where the events of the pilot failed to encourage these three, key psychological needs, both students and teachers were less motivated and engaged with the project

The Role of Explainability in Assuring Safety of Machine Learning in Healthcare

Established approaches to assuring safety-critical systems and software are difficult to apply to systems employing ML where there is no clear, pre-defined specification against which to assess validity. This problem is exacerbated by the opaque nature of ML where the learnt model is not amenable to human scrutiny. XAI methods have been proposed to tackle this issue by producing human-interpretable representations of ML models which can help users to gain confidence and build trust in the ML system. However, little work explicitly investigates the role of explainability for safety assurance in the context of ML development. This paper identifies ways in which XAI methods can contribute to safety assurance of ML-based systems. It then uses a concrete ML-based clinical decision support system, concerning weaning of patients from mechanical ventilation, to demonstrate how XAI methods can be employed to produce evidence to support safety assurance. The results are also represented in a safety argument to show where, and in what way, XAI methods can contribute to a safety case. Overall, we conclude that XAI methods have a valuable role in safety assurance of ML-based systems in healthcare but that they are not sufficient in themselves to assure safety

Independent components in spectroscopic analysis of complex mixtures

We applied two methods of "blind" spectral decomposition (MILCA and SNICA) to quantitative and qualitative analysis of UV absorption spectra of several non-trivial mixture types. Both methods use the concept of statistical independence and aim at the reconstruction of minimally dependent components from a linear mixture. We examined mixtures of major ecotoxicants (aromatic and polyaromatic hydrocarbons), amino acids and complex mixtures of vitamins in a veterinary drug. Both MICLA and SNICA were able to recover concentrations and individual spectra with minimal errors comparable with instrumental noise. In most cases their performance was similar to or better than that of other chemometric methods such as MCR-ALS, SIMPLISMA, RADICAL, JADE and FastICA. These results suggest that the ICA methods used in this study are suitable for real life applications. Data used in this paper along with simple matlab codes to reproduce paper figures can be found at http://www.klab.caltech.edu/~kraskov/MILCA/spectraComment: 22 pages, 4 tables, 6 figure