Assessing Automated Administration

To fulfill their responsibilities, governments rely on administrators and employees who, simply because they are human, are prone to individual and group decision-making errors. These errors have at times produced both major tragedies and minor inefficiencies. One potential strategy for overcoming cognitive limitations and group fallibilities is to invest in artificial intelligence (AI) tools that allow for the automation of governmental tasks, thereby reducing reliance on human decision-making. Yet as much as AI tools show promise for improving public administration, automation itself can fail or can generate controversy. Public administrators face the question of when exactly they should use automation. This paper considers the justifications for governmental reliance on AI along with the legal concerns raised by such reliance. Comparing AI-driven automation with a status quo that relies on human decision-making, the paper provides public administrators with guidance for making decisions about AI use. After explaining why prevailing legal doctrines present no intrinsic obstacle to governmental use of AI, the paper presents considerations for administrators to use in choosing when and how to automate existing processes. It recommends that administrators ask whether their contemplated uses meet the preconditions for the deployment of AI tools and whether these tools are in fact likely to outperform the status quo. In moving forward, administrators should also consider the possibility that a contemplated AI use will generate public or legal controversy, and then plan accordingly. The promise and legality of automated administration ultimately depends on making responsible decisions about when and how to deploy this technology

Toward Realism in Human Performance Simulation

This chapter focuses on challenges to improving the realism of socially intelligent agents and attempts to reflect the state of the art in human behavior modeling with particular attention to the impact of values, emotion, and physiology/stress upon individual and group decision-making. The goal is to help those interested in constructing more realistic software agents for use in human performance simulations in both training and analysis settings. The first two sections offer an assessment of the state of the practice and of the need to make better use of human performance moderator functions (PMFs) published in the behavioral literature. The third section pursues this goal by providing an illustrative framework for integrating existing PMF theories and models, such as those on physiology and stress, cognitive and emotive processes, individual differences, and group and crowd behavior, among others. The fourth section presents asymmetric warfare and civil unrest case studies to examine some of the concerns affecting implementation of PMFs such as verification, validation, and interoperability with existing simulators, artificial life emulators, and artificial intelligence components. The final section of this chapter concludes with lessons learned and with some challenges if the field is to reach a greater level of maturity

Role of Artificial Intelligence (AI) art in care of ageing society: focus on dementia

open access articleBackground: Art enhances both physical and mental health wellbeing. The health benefits include reduction in blood pressure, heart rate, pain perception and briefer inpatient stays, as well as improvement of communication skills and self-esteem. In addition to these, people living with dementia benefit from reduction of their noncognitive, behavioural changes, enhancement of their cognitive capacities and being socially active. Methods: The current study represents a narrative general literature review on available studies and knowledge about contribution of Artificial Intelligence (AI) in creative arts. Results: We review AI visual arts technologies, and their potential for use among people with dementia and care, drawing on similar experiences to date from traditional art in dementia care. Conclusion: The virtual reality, installations and the psychedelic properties of the AI created art provide a new venue for more detailed research about its therapeutic use in dementia

Artificial intelligence and UK national security: Policy considerations

RUSI was commissioned by GCHQ to conduct an independent research study into the use of artificial intelligence (AI) for national security purposes. The aim of this project is to establish an independent evidence base to inform future policy development regarding national security uses of AI. The findings are based on in-depth consultation with stakeholders from across the UK national security community, law enforcement agencies, private sector companies, academic and legal experts, and civil society representatives. This was complemented by a targeted review of existing literature on the topic of AI and national security. The research has found that AI offers numerous opportunities for the UK national security community to improve efficiency and effectiveness of existing processes. AI methods can rapidly derive insights from large, disparate datasets and identify connections that would otherwise go unnoticed by human operators. However, in the context of national security and the powers given to UK intelligence agencies, use of AI could give rise to additional privacy and human rights considerations which would need to be assessed within the existing legal and regulatory framework. For this reason, enhanced policy and guidance is needed to ensure the privacy and human rights implications of national security uses of AI are reviewed on an ongoing basis as new analysis methods are applied to data

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM