Data-driven E-Government: Exploring the Socio-Economic Ramifications

The evident benefits of big data, artificial intelligence and machine learning in society have begun to influence the transition towards a data-driven public sector. Decision-making in the public sector is in an infancy phase of a revolution owing to the inclusion of these new technological innovations. Research has revealed that data-driven e-government policies improve socio-economic development in some nations. Despite the immense opportunities data-driven e-government models have for governments, similar to every system, there are ramifications. This study explores the concept of data-driven e-government as well as investigates the socio-economic implications such an e-government model can have on society. Findings of this exploratory study add insight into a field which is in its early days and still unfocused, as well as making recommendations for policymakers.

CO2 Efficiency Break Points for Processes Associated to Wood and Coal Transport and Heating

This paper aims to deal with CO2 emissions in energy production process in an original way, based on calculations of total specific CO2 emissions, depending on the type of fuel and the transport distance. This paper has ambition to set a break point from where it is not worthwhile to use wood as an energy carrier as the alternative to coal. The reason for our study is the social urgency of selected problem. For example, in the area of public sector decision-making, wood heating is promoted regardless of the availability within the reasonable distance. From the current state of the research, it is also clear that none of the studies compare coal and biomass fuel transportation from the point of view of CO2 production. For this purpose, an original methodology has been proposed. It is based on a modified life cycle assessment (LCA), supplemented with a system of equations. The proposed methodology has a generalizable nature, and therefore, it can be applied to different regions. However, calculation inputs and modelling are based on specific site data. Based on the presented numerical analysis, the key finding is the break point for associated processes at a distance of 1779.64 km, since when that it is better to burn brown coal than wood in terms of total CO2 emissions. We can conclude that, in some cases, it is more efficient to use coal instead of wood as fuel in terms of CO2 emissions, particularly in regard to transport distance and type of transport.

Administrative law and the machines of government: judicial review of automated public-sector decision-making

The future is likely to see an increase in the public-sector use of automated decision-making systems which employ machine learning techniques. However, there is no clear understanding of how English administrative law will apply to this kind of decision-making. This paper seeks to address the problem by bringing together administrative law, data protection law, and a technical understanding of automated decision-making systems in order to identify some of the questions to ask and factors to consider when reviewing the use of these systems. Due to the relative novelty of automated decision-making in the public sector, this kind of study has not yet been undertaken elsewhere. As a result, this paper provides a starting point for judges, lawyers, and legal academics who wish to understand how to legally assess or review automated decision-making systems and identifies areas where further research is required.

Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making

Cite as:Michael Veale, Max Van Kleek and Reuben Binns (2018) Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making. ACM Conference on Human Factors in Computing Systems (CHI'18). doi: 10.1145/3173574.3174014Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions—like taxation, justice, and child protection—are now commonplace. How might designers support such human values? We interviewed 27 public sector machine learning practitioners across 5 OECD countries regarding challenges understanding and imbuing public values into their work. The results suggest a disconnect between organisational and institutional realities, constraints and needs, and those addressed by current research into usable, transparent and 'discrimination-aware' machine learning—absences likely to undermine practical initiatives unless addressed. We see design opportunities in this disconnect, such as in supporting the tracking of concept drift in secondary data sources, and in building usable transparency tools to identify risks and incorporate domain knowledge, aimed both at managers and at the `street-level bureaucrats' on the frontlines of public service. We conclude by outlining ethical challenges and future directions for collaboration in these high-stakes applications.