Assessing the public policy-cycle framework in the age of artificial intelligence: From agenda-setting to policy evaluation

Does Trust Lead to the Adoption of a Productive Climate Attitude? Relationship Between Trust, Corruption, and Climate Attitude in Developing Regions

This study examines the relationship between institutional trust from an individual and societal perspective and perceived corruption and climate attitudes of individuals in Latin America. To this end, multilevel modeling was used to test whether the attitudes of individuals from 285 regions of Latin America are influenced by these constructs. Based on the results, it was found that in contrast to studies in developed countries, where institutional trust is positively associated with pro-climate attitudes, in Latin America institutional trust acts as an inhibiting factor and is inversely related to climate attitudes. Furthermore, the perception of corruption in public institutions was also identified as a factor inhibiting collective action to combat climate change. Moderation analysis revealed that individuals' level of education significantly influences this relationship, with a notable difference in climate attitudes between individuals with low and high levels of trust, especially among those with less education. These findings highlight the importance of taking regional specificities into account when examining the relationship between institutional trust, perceptions of corruption, and climate attitudes, and underscore the need for public policies that promote transparency and accountability of institutions to foster effective collective action on climate change.

Precision public health, the key for future outbreak management: A scoping review

Background

Precision Public Health (PPH) is a newly emerging field in public health medicine. The application of various types of data allows PPH to deliver more tailored interventions to a specific population within a specific timeframe. However, the application of PPH possesses several challenges and limitations that need to be addressed.

Objective

We aim to provide evidence of the various use of PPH in outbreak management, the types of data that could be used in PPH application, and the limitations and barriers in the application of the PPH approach.

Methods and analysis

Articles were searched in PubMed, Web of Science, and Science Direct. Our selection of articles was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for Scoping Review guidelines. The outcome of the evidence assessment was presented in narrative format instead of quantitative.

Results

A total of 27 articles were included in the scoping review. Most of the articles (74.1%) focused on PPH applications in performing disease surveillance and signal detection. Furthermore, the data type mostly used in the studies was surveillance (51.9%), environment (44.4), and Internet query data. Most of the articles emphasized data quality and availability (81.5%) as the main barriers in PPH applications followed by data integration and interoperability (29.6%).

Conclusions

PPH applications in outbreak management utilize a wide range of data sources and analytical techniques to enhance disease surveillance, investigation, modeling, and prediction. By leveraging these tools and approaches, PPH contributes to more effective and efficient outbreak management, ultimately reducing the burden of infectious diseases on populations. The limitation and challenges in the application of PPH approaches in outbreak management emphasize the need to strengthen the surveillance systems, promote data sharing and collaboration among relevant stakeholders, and standardize data collection methods while upholding privacy and ethical principles.

AI for Anglophone Africa: Unlocking its adoption for responsible solutions in academia-private sector

In recent years, AI technologies have become indispensable in social and industrial development, yielding revolutionary results in improving labor efficiency, lowering labor costs, optimizing human resource structure, and creating new job demands. To reap the full benefits of responsible AI solutions in Africa, it is critical to investigate existing challenges and propose strategies, policies, and frameworks for overcoming and eliminating them. As a result, this study investigated the challenges of adopting responsible AI solutions in the Academia-Private sectors for Anglophone Africa through literature reviews, expert interviews, and then proposes solutions and framework for the sustainable and successful adoption of responsible AI.

The promises and perils of populism for democratic policymaking: the case of Mexico

Much has been said theoretically about whether populism corrects the limitations of democracies, or instead damages their foundations. Yet we still know very little about how populist governments affect democratic policymaking in practice. Taking the classic policy cycle approach as a heuristic device, this article analyzes how populists influence agenda-setting, policy formulation and design, implementation, evaluation, and termination processes. Using a variety of sources, the article provides a qualitative in-depth analysis of the Mexican case during the first half of president Andrés-Manuel López-Obrador administration. The article shows that a populist government may fulfill some of its promises, but it ultimately materializes most of its perils, causing significant policy, institutional, and social damage. Populists introduce important distortions in each one of the policy stages and thus alter considerably the policymaking processes usually associated with democratic regimes. They employ a variety of strategies to limit the number of policy actors taking part in agenda-setting and evaluation exercises; formulate ineffective policy tools based on questionable design assumptions; develop personalistic implementation channels prone to patronage and clientelism; undermine the value of evidence-based analyses and discussions; and terminate institutions and programs on a discretionary basis. By exerting a rhetorical monopoly over the 'will of the people,' populists can follow policymaking patterns that significantly depart from the technical, rational, and pluralistic standards commonly associated with democratic policymaking. The article brings together debates on populism and policymaking, and studies a national case which has received limited scholarly attention, thus adding to both our theoretical and empirical contemporary understanding on this subject.

Social media use in government health agencies: The COVID-19 impact

Government communications typically use social media for federal agencies, national policies, and emergency cases. However, it is crucial to understand the use of these platforms within local government agencies. This investigation fills this gap and includes social media data during three months of the COVID-19 pandemic and compares it with previous trends. The research question that leads this research is: How has government social media use and communication behavior changed due to the COVID-19 pandemic? This research analyzes a study case applied to a local Mexican government agency named ISSEMyM. We analyze the data during the period between October 2017 and June 2020. Findings reveal a consistent use of Twitter and Facebook for communication by the ISSEMyM government agency. Also, it indicates an increase in engagement and communication flow over three years. We found that official local government communications remained unchanged during the whole period. We found no change in interaction, number of followers, and message structure during this period; however, the COVID-19 pandemic radically alters interactions, followers, and user engagement.

Organiational Structure and Created Values. Review of Methods of Studying Collective Intelligence in Policymaking

The domain of policymaking, which used to be limited to small groups of specialists, is now increasingly opening up to the participation of wide collectives, which are not only influencing government decisions, but also enhancing citizen engagement and transparency, improving service delivery and gathering the distributed wisdom of diverse participants. Although collective intelligence has become a more common approach to policymaking, the studies on this subject have not been conducted in a systematic way. Nevertheless, we hypothesized that methods and strategies specific to different types of studies in this field could be identified and analyzed. Based on a systematic literature review, as well as qualitative and statistical analyses, we identified 15 methods and revealed the dependencies between them. The review indicated the most popular approaches, and the underrepresented ones that can inspire future research.

Artificial Intelligence Risks and Challenges in the Spanish Public Administration: An Exploratory Analysis through Expert Judgements

The expanding use of artificial intelligence (AI) in public administration is generating numerous opportunities for governments. Current Spanish regulations have established electronic administration and support the expansion and implementation of this new technology, but they may not be adapted to the legal needs caused by AI. Consequently, this research aims to identify the risks associated with AI uses in Spanish public administration and if the legal mechanisms can solve them. We answer these questions by employing a qualitative research approach, conducting semi-structured interviews with several experts in the matter. Despite the benefits that this technology may involve, throughout this research we can confirm that the use of artificial intelligence can generate several problems such as opacity, legal uncertainty, biases, or breaches of personal data protection. The mechanisms already provided by Spanish law are not enough to avoid these risks as they have not been designed to face the use of artificial intelligence in public administration. In addition, a homogeneous legal definition of AI needs to be established.

Soul of a new machine: Self-learning algorithms in public administration

Big data sets in conjunction with self-learning algorithms are becoming increasingly important in public administration. A growing body of literature demonstrates that the use of such technologies poses fundamental questions about the way in which predictions are generated, and the extent to which such predictions may be used in policy making. Complementing other recent works, the goal of this article is to open the machine’s black box to understand and critically examine how self-learning algorithms gain agency by transforming raw data into policy recommendations that are then used by policy makers. I identify five major concerns and discuss the implications for policy making.

Feminist perspectives to artificial intelligence: Comparing the policy frames of the European Union and Spain

Artificial Intelligence (AI) is a disruptive technology that has gained interest among scholars, politicians, public servants, and citizens. In the debates on its advantages and risks, issues related to gender have arisen. In some cases, AI approaches depict a tool to promote gender equality, and in others, a contribution to perpetuating discrimination and biases. We develop a theoretical and analytical framework, combining the literature on technological frames and gender theory to better understand the gender perspective of the nature, strategy, and use of AI in two institutional contexts. Our research question is: What are the assumptions, expectations and knowledge of the European Union institutions and Spanish government on AI regarding gender? Methodologically, we conducted a document analysis of 23 official documents about AI issued by the European Union (EU) and Spain to understand how they frame the gender perspective in their discourses. According to our analysis, despite both the EU and Spain have developed gender-sensitive AI policy frames, doubts remain about the definitions of key terms and the practical implementation of their discourses.

Algorithmic transparency and bureaucratic discretion: The case of SALER early warning system

The governance of public sector organizations has been challenged by the growing adoption and use of Artificial Intelligence (AI) systems and algorithms. Algorithmic transparency, conceptualized here using the dimensions of accessibility and explainability, fosters the appraisal of algorithms’ footprint in decisions of public agencies, and should include impacts on civil servants’ work. However, although discretion will not disappear, AI innovations might have a negative impact on how public employees support their decisions. This article is intended to answer the following research questions: RQ1. To what extent algorithms affect discretionary power of civil servants to make decisions?RQ2. How algorithmic transparency can impact discretionary power of civil servants? To do so, we analyze SALER, a case based on a set of algorithms focused on the prevention of irregularities in the Valencian regional administration (GVA), Spain, using a qualitative methodology supported on semi-structured interviews and documentary analysis. Among the results of the study, our empirical work suggests the existence of a series of factors that might be linked to the positive impacts of algorithms on the work and discretionary power of civil servants. Also, we identify different pathways for achieving algorithmic transparency, such as the involvement of civil servants in active development, or auditing processes being recognized by law, among others.