Studies of accounting in prisons have documented how the dominance of neo-liberal preferences in the management of prison systems has meant a focus on measuring costeffectiveness and discharging accountability in a way which ignores important aspects of prison life such as decency and rehabilitation. The present work recognises the need for accounting studies to unmask the prejudiced, interested and potentially socially damaging use of accounting in prison reform by exposing how accounting is often a tool which can be used to discipline individuals consistent with the values of economic and political elites within a society. By applying a Foucauldian lens to the analysis of the prison systems of the major Italian States in the early 19th century, the study shows how accounting was an essential aid in ensuring that the correction of individuals was carried out consistent with the moral priorities of the liberal bourgeois society of the time. The specific visibilities created by means of accounting, upon which decisions on the life of prisoners were made and which still characterise modern prisons, reduced human life to a simplistic numerical representation. These were then used to reconstruct ‘deviant’ individuals as productive, obedient citizens, regardless of their specific circumstances
Keywords: Accounting | Prison | Foucault | Liberal values

Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive maintenance decisions, whereby a failure is prevented through timely repair or replacement. This allows for a better decision support by considering the anticipated time-to-failure and thus promises to reduce costs. Here a common baseline may be derived by fitting a probability density function to past lifetimes and then utilizing the (conditional) expected remaining useful life as a prognostic. This approach finds widespread use in practice because of its high explanatory power. A more accurate alternative is promised by machine learning, where forecasts incorporate deterioration processes and environmental variables through sensor data. However, machine learning largely functions as a black-box method and its forecasts thus forfeit most of the desired interpretability. As our primary contribution, we propose a structured-effect neural network for predicting the remaining useful life which combines the favorable properties of both approaches: its key innovation is that it offers both a high accountability and the flexibility of deep learning. The parameters are estimated via variational Bayesian inferences. The different approaches are compared based on the actual time-to-failure for aircraft engines. This demonstrates the performance and superior interpretability of our method, while we finally discuss implications for decision support.
Keywords: Forecasting | Remaining useful life | Machine learning | Neural networks | Deep learning

This study examines issues of success/failure, performance, and effectiveness in the contemporary reform discourses of school accountability. It pays attention to a particular meaning of success/failure and its implications for current public school systems and education reform movements, not merely as a school accountability issue, but as an ontological one, which is inscribed in the politics of inclusion and exclusion. The mechanisms of standardization, classification, and normalization embedded in the practices of high-stakes testing are reconsidered through an analysis of the discussion of social efficiency in the early twentieth century. The study also examines its recall several decades later as part of the quality control and management of individuals, schools, and states. In exploring connectedness between the old and new levels of social efficiency, this study suggests that success/failure becomes reinscribed as a particular system of reasoning to normalize the subjectivity in discourses of the current American education reform and OECD’s PISA. The study concludes by criticizing the rationale of recent educational reforms based on the search for past and present social efficiency movements in schools.
keywords: Social efficiency movement |Scientific management |Standard-based education reform |OECD PISA

Inspired by the literature on processing fluency and the practical question of how to choose fonts for website design, we investigated the impact of customer reviews on consumer evaluation under an easy-to-read (vs. difficult-to-read) review font. We found that the feeling of ease in reading led consumers to judge the reviewers as more credible, thus increasing the impact of the reviews. Moreover, the effect of font diminished when consumers elaborated information more thoroughly (i.e., when high in need for cognition, or in an accountable situation) and thus did not use simple heuristics (reading ease) to make judgment.
keywords: Processing ease| Review valence| Online customer reviews| Credibility| Need for cognition| Accountability

There is an abundance of enthusiasm and optimism about how governments at all levels can make use of big data, algorithms and artificial intelligence. There is also growing concern about the risks that come with these new systems. This article makes the case for greater government transparency and accountability about uses of big data through a Government of Canada qualitative research case study. Adapting a method from critical cartographers, I employ countermapping to map government big data practices and internal discussions of risk and challenge. I do so by drawing on interviews and freedom of information requests. The analysis reveals that there are more concerns and risks than often publicly discussed and that there are significant areas of silence that need greater attention. The article underlines the need for our democratic systems to respond to our new datafied contexts by ensuring that our institutions make changes to better protect citizen rights, uphold democratic principles and ensure means for citizen intervention.
Keywords: Big data | government | democracy | transparency | accountability