There is currently considerable excitement within government about the potential of artificial intelligence to improve public service productivity through the automation of complex but repetitive bureaucratic tasks, freeing up the time of skilled staff. Here, we explore the size of this opportunity, by mapping out the scale of citizen-facing bureaucratic decision-making procedures within UK central government, and measuring their potential for AI-driven automation. We estimate that UK central government conducts approximately one billion citizen-facing transactions per year in the provision of around 400 services, of which approximately 143 million are complex repetitive transactions. We estimate that 84% of these complex transactions are highly automatable, representing a huge potential opportunity: saving even an average of just one minute per complex transaction would save the equivalent of approximately 1,200 person-years of work every year. We also develop a model to estimate the volume of transactions a government service undertakes, providing a way for government to avoid conducting time consuming transaction volume measurements. Finally, we find that there is high turnover in the types of services government provide, meaning that automation efforts should focus on general procedures rather than services themselves which are likely to evolve over time. Overall, our work presents a novel perspective on the structure and functioning of modern government, and how it might evolve in the age of artificial intelligence.

Artificial intelligence, machine learning, and algorithmic techniques in general, provide two crucial abilities with the potential to improve decision-making in the context of allocation of scarce societal resources. They have the ability to flexibly and accurately model treatment response at the individual level, potentially allowing us to better match available resources to individuals. In addition, they have the ability to reason simultaneously about the effects of matching sets of scarce resources to populations of individuals. In this work, we leverage these abilities to study algorithmic allocation of scarce societal resources in the context of homelessness. In communities throughout the United States, there is constant demand for an array of homeless services intended to address different levels of need. Allocations of housing services must match households to appropriate services that continuously fluctuate in availability, while inefficiencies in allocation could “waste” scarce resources as households will remain in-need and re-enter the homeless system, increasing the overall demand for homeless services. This complex allocation problem introduces novel technical and ethical challenges. Using administrative data from a regional homeless system, we formulate the problem of “optimal” allocation of resources given data on households with need for homeless services. The optimization problem aims to allocate available resources such that predicted probabilities of household re-entry are minimized. The key element of this work is its use of a counterfactual prediction approach that predicts household probabilities of re-entry into homeless services if assigned to each service. Through these counterfactual predictions, we find that this approach has the potential to improve the efficiency of the homeless system by reducing re-entry, and, therefore, system-wide demand. However, efficiency comes with trade-offs - a significant fraction of households are assigned to services that increase probability of re-entry. To address this issue as well as the inherent fairness considerations present in any context where there are insufficient resources to meet demand, we discuss the efficiency, equity, and fairness issues that arise in our work and consider potential implications for homeless policies.

Working in the UX industry means living in a constant state of flux. Every day seems to bring new technologies, skills, business challenges, and user expectations to absorb. That's why every December, I eagerly await the release of UX Collective's State of UX report, in which authors Fabricio Teixeira and Caio Braga (plus collaborators) synthesize thousands of articles in order to put the past year into perspective and highlight emerging themes for the road ahead. The underlying theme for this year's report was anxiety. Massive layoffs at tech stalwarts like Facebook, Google, and Amazon, along with headlines about an economic slowdown, have some designers thinking about how to recession-proof their jobs. With headcounts shrinking, design teams are expected to do more with less, and former managers are returning to hands-on work.

We are facing a global healthcare crisis today as the healthcare cost is ever climbing, but with the aging population, government fiscal revenue is ever dropping. To create a more efficient and effective healthcare system, three technical challenges immediately present themselves: healthcare access, healthcare equity, and healthcare efficiency. An autonomous mobile clinic solves the healthcare access problem by bringing healthcare services to the patient by the order of the patient's fingertips. Nevertheless, to enable a universal autonomous mobile clinic network, a three-stage technical roadmap needs to be achieved: In stage one, we focus on solving the inequity challenge in the existing healthcare system by combining autonomous mobility and telemedicine. In stage two, we develop an AI doctor for primary care, which we foster from infancy to adulthood with clean healthcare data. With the AI doctor, we can solve the inefficiency problem. In stage three, after we have proven that the autonomous mobile clinic network can truly solve the target clinical use cases, we shall open up the platform for all medical verticals, thus enabling universal healthcare through this whole new system.

Information Technology Service Management (ITSM) helps in monitoring, analyzing, and improving the quality of IT services to enhance customer satisfaction. On the other hand, Artificial Intelligence for IT Operations (AIOps) aims at providing self-learning and intelligent solutions to run IT systems smoothly by optimizing the resources available. But what if these two could work together? What if they could help each other out? Well, they can, and they do!

Now more than ever, public sector's digital experience is under scrutiny. Government departments and agencies' quality of provided services is being compared to the best, frictionless digital experiences offered by the private sector, and the result is too many public sector organizations around the world are relying on manual business processes, rather than innovative technology, leading to inefficiencies and frustrated citizens. Following the pandemic, it is clear that digital transformation is becoming a necessity. Many organizations are already fast-tracking their digital transformation programs to build resilience and improve productivity. Implementing intelligent automation technologies (IATs) can deliver cost reductions of up to 90%, process paperwork up to 10 times faster and deliver 67% more accuracy across paper-intensive processes.

Convergint, a global leader in service-based systems integration, announced plans to acquire Prosys Services, an Australian tier-one provider in security management. Prosys Services is Convergint's third acquisition in Australia, adding 124 colleagues, a deepened presence in New South Wales (NSW), and a new office in the Australian Capital Territory (ACT). "This is another important milestone in expanding our capabilities and service delivery in Oceania, and in our mission to be our customers' best service provider around the globe." "We're thrilled to bring Prosys' team of talented colleagues to Convergint," said Tony Wang, CEO of ICD Security Solutions, Convergint's APAC subsidiary. "From its focus on continuous development to its strong company culture, Prosys has an incredible reputation built on a long-standing commitment to innovating for the future of security technology. This acquisition further accelerates our growth strategy in APAC and allows us to extend our best-in-class service delivery to global enterprises in the region."

Gartner, Inc. identified the top 10 government technology trends for 2022 that can guide public-sector leaders in accelerating digital transformation and mitigating disruption risks. "Government and public sector CIOs now need to sustain the momentum of digital acceleration after the initial chaos of the pandemic. CIOs can use these top trends to establish future-ready organisations by demonstrating how digital initiatives deliver value to diverse and evolving constituent needs, support new workforce trends, enable efficient scaling of operations and build a composable business and technology foundation," said Arthur Mickoleit, Research Director, Gartner. Government CIOs must consider the collective impact of the following 10 trends on their organisations and include them in their strategic plans for 2022 and beyond. Not doing so risks undermining the quality of government services and the capacity to deliver mission value in the long run.

One of the core envisions of the sixth-generation (6G) wireless networks is to accumulate artificial intelligence (AI) for autonomous controlling of the Internet of Everything (IoE). Particularly, the quality of IoE services delivery must be maintained by analyzing contextual metrics of IoE such as people, data, process, and things. However, the challenges incorporate when the AI model conceives a lake of interpretation and intuition to the network service provider. Therefore, this paper provides an explainable artificial intelligence (XAI) framework for quality-aware IoE service delivery that enables both intelligence and interpretation. First, a problem of quality-aware IoE service delivery is formulated by taking into account network dynamics and contextual metrics of IoE, where the objective is to maximize the channel quality index (CQI) of each IoE service user. Second, a regression problem is devised to solve the formulated problem, where explainable coefficients of the contextual matrices are estimated by Shapley value interpretation. Third, the XAI-enabled quality-aware IoE service delivery algorithm is implemented by employing ensemble-based regression models for ensuring the interpretation of contextual relationships among the matrices to reconfigure network parameters. Finally, the experiment results show that the uplink improvement rate becomes 42.43% and 16.32% for the AdaBoost and Extra Trees, respectively, while the downlink improvement rate reaches up to 28.57% and 14.29%. However, the AdaBoost-based approach cannot maintain the CQI of IoE service users. Therefore, the proposed Extra Trees-based regression model shows significant performance gain for mitigating the trade-off between accuracy and interpretability than other baselines.

Dr Adrian Engelbrecht, Product & Development Lead, Serviceware AI, looks at how AI is taking quality and cost optimization of enterprise services to the next level. Business and service leaders are under more pressure than ever. Nearly two years on from the initial rumblings of the pandemic and Europe's already fragile economic recovery is at further risk as a series of potential restrictions are expected to put the brakes on business growth. In this environment, cost is a top priority, but so is keeping service customers satisfied. While budgets are being squeezed, businesses must still ensure service performance is optimized.