Currently, artificial intelligence (AI) is integrated across various segments of the public sector, in a scattered and fragmented manner, aiming to enhance the quality of people’s lives. While AI adoption has proven to have a great impact, there are several aspects that hamper its utilization in public administration. Therefore, a large set of initiatives is designed to play a pivotal role in promoting the adoption of reliable AI, including documentation as a key driver. The AI community has been proactively recommending a variety of initiatives aimed at promoting the adoption of documentation practices. While currently proposed AI documentation artifacts play a crucial role in increasing the transparency and accountability of various facts about AI systems, we propose a code-bound declarative documentation framework that aims to support the responsible deployment of AI-based solutions. Our proposed framework aims to address the need to shift the focus from data and models being considered in isolation to the reuse of AI solutions as a whole. By introducing a formalized approach to describing adaptation and optimization techniques, we aim to enhance existing documentation alternatives. Furthermore, its utilization in the public administration aims to foster the rapid adoption of AI-based applications due to the open access to common use cases in the public sector. We further showcase our proposal with a public sector-specific use case, such as a legal text classification task, and demonstrate how the AI Product Card enables its reuse through the interactions of the formal documentation specifications with the modular code references.

The global number of individuals experiencing forced displacement has reached its highest level in the past decade. In this context, the provision of services for those in need requires timely and evidence-based approaches. How can mobile phone data (MPD) based analyses address the knowledge gap on mobility patterns and needs assessments in forced displacement settings? To answer this question, in this paper, we examine the capacity of MPD to function as a tool for anticipatory analysis, particularly in response to natural disasters and conflicts that lead to internal or cross-border displacement. The paper begins with a detailed review of the processes involved in acquiring, processing, and analyzing MPD in forced displacement settings. Following this, we critically assess the challenges associated with employing MPD in policy-making, with a specific focus on issues of user privacy and data ethics. The paper concludes by evaluating the potential benefits of MPD analysis for targeted and effective policy interventions and discusses future research avenues, drawing on recent studies and ongoing collaborations with mobile network operators.

In this paper, we provide a systematic review of existing artificial intelligence (AI) regulations in Europe, the United States, and Canada. We build on the qualitative analysis of 129 AI regulations (enacted and not enacted) to identify patterns in regulatory strategies and in AI transparency requirements. Based on the analysis of this sample, we suggest that there are three main regulatory strategies for AI: AI-focused overhauls of existing regulation, the introduction of novel AI regulation, and the omnibus approach. We argue that although these types emerge as distinct strategies, their boundaries are porous as the AI regulation landscape is rapidly evolving. We find that across our sample, AI transparency is effectively treated as a central mechanism for meaningful mitigation of potential AI harms. We therefore focus on AI transparency mandates in our analysis and identify six AI transparency patterns: human in the loop, assessments, audits, disclosures, inventories, and red teaming. We contend that this qualitative analysis of AI regulations and AI transparency patterns provides a much needed bridge between the policy discourse on AI, which is all too often bound up in very detailed legal discussions and applied sociotechnical research on AI fairness, accountability, and transparency.

The “la Caixa” Foundation has been experimenting with artificial intelligence (AI)-assisted decision-making geared toward alleviating the administrative burden associated with the evaluation pipeline of its flagship funding program, piloting an algorithm to detect immature project proposals before they reach the peer review stage, and suggest their removal from the selection process to a human overseer. In this article, we explore existing uses of AI by publishers and research funding organizations to automate their selection pipelines, in addition to analyzing the conditions under which the focal case corresponds to a responsible use of AI and the extent to which these conditions are met by the current implementation, highlighting challenges and areas of improvement.

As the field of migration studies evolves in the digital age, big data analytics emerge as a potential game-changer, promising unprecedented granularity, timeliness, and dynamism in understanding migration patterns. However, the epistemic value added by this data explosion remains an open question. This paper critically appraises the claim, investigating the extent to which big data augments, rather than merely replicates, traditional data insights in migration studies. Through a rigorous literature review of empirical research, complemented by a conceptual analysis, we aim to map out the methodological shifts and intellectual advancements brought forth by big data. The potential scientific impact of this study extends into the heart of the discipline, providing critical illumination on the actual knowledge contribution of big data to migration studies. This, in turn, delivers a clarified roadmap for navigating the intersections of data science, migration research, and policymaking.

During the 20th century, dealing with grief through an ongoing involvement with the deceased (such as speaking to their grave) was seen as pathological by Western authors such as Sigmund Freud. Nowadays, we are presented with the opportunity to continue interacting with digital representations of the deceased. As a result, the paper adopts an Ubuntu perspective, i.e., a sub-Saharan African philosophy focussed on community and relationship to provide a toolkit for using this emerging technology. I will argue that the Ubuntu framework I propose contributes to the use of griefbots in two ways. The first is that it shows that it is morally permissible to use griefbots to assuage our grief. The second is that it delineates how we can ethically use the technology. To do so, I split my analysis into four sections. In the first section, I show that meaningful relationships can occur between the bereaved and griefbots. This will be done by exploring the Western theory of continuing bonds proposed by Dennis Klass, Phyllis Silverman and Steven Nickman. In my second, I flesh out my Ubuntu framework according to Thaddeus Metz’s accounts on Ubuntu as a modal-relational theory. In my third section, I apply my Ubuntu framework to the case of Roman Mazurenko. Furthermore, I consider some counterarguments to the Ubuntu framework regarding privacy, commercialisation and people replacement. Finally, I conclude that, despite these limitations, the Ubuntu framework positively contributes to determining whether we should communicate with the dead through griefbots to assuage our grief.

Open data promises various benefits, including stimulating innovation, improving transparency and public decision-making, and enhancing the reproducibility of scientific research. Nevertheless, numerous studies have highlighted myriad challenges related to preparing, disseminating, processing, and reusing open data, with newer studies revealing similar issues to those identified a decade prior. Several researchers have proposed the open data ecosystem (ODE) as a lens for studying and devising interventions to address these issues. Since actors in the ecosystem are individually and collectively impacted by the sustainability of the ecosystem, all have a role in tackling the challenges in the ODE. This paper asks what the contributions of open data intermediaries may be in addressing these challenges. Open data intermediaries are third-party actors providing specialized resources and capabilities to (i) enhance the supply, flow, and/or use of open data and/or (ii) strengthen the relationships among various open data stakeholders. They are critical in ensuring the flow of resources within the ODE. Through semi-structured interviews and a validation exercise in the European Union context, this study explores the potential contribution of open data intermediaries and the specific ODE challenges they may address. This study identified 20 potential contributions, addressing 27 challenges. The findings of this study pave the way for further inquiry into the internal incentives (viable business models) and external incentives (policies and regulations) to direct the contributions of open data intermediaries toward addressing challenges in the ODE.

This study focuses on the practicalities of establishing and maintaining AI infrastructure, as well as the considerations for responsible governance by investigating the integration of a pre-trained large language model (LLM) with an organisation’s knowledge management system via a chat interface. The research adopts the concept of “AI as a constituted system” to emphasise the social, technical, and institutional factors that contribute to AI’s governance and accountability. Through an ethnographic approach, this article details the iterative processes of negotiation, decision-making, and reflection among organisational stakeholders as they develop, implement, and manage the AI system. The findings indicate that LLMs can be effectively governed and held accountable to stakeholder interests within specific contexts, specifically, when clear institutional boundaries facilitate innovation while navigating the risks related to data privacy and AI misbehaviour. Effective constitution and use can be attributed to distinct policy creation processes to guide AI’s operation, clear lines of responsibility, and localised feedback loops to ensure accountability for actions taken. This research provides a foundational perspective to better understand algorithmic accountability and governance within organisational contexts. It also envisions a future where AI is not universally scaled but consists of localised, customised LLMs tailored to stakeholder interests.

Ethical guidelines and policy documents destined to guide AI innovations have been heralded as the solution to guard us against harmful effects or to increase public value. However, these guidelines and policy documents face persistent challenges. While these documents are often criticized for their abstraction and disconnection from real-world contexts, it also occurs that stakeholders may influence them for political or strategic reasons. While this last issue is frequently acknowledged, there is seldom a means or a method provided to explore it. To address this gap, the paper employs a combination of social constructivism and science & technology studies perspectives, along with desk research, to investigate whether prior research has examined the influence of stakeholder interests, strategies, or agendas on guidelines and policy documents. The study contributes to the discourse on AI governance by proposing a theoretical framework and methodologies to better analyze this underexplored area, aiming to enhance comprehension of the policymaking process within the rapidly evolving AI landscape. The findings underscore the need for a critical evaluation of the methodologies found and a further exploration of their utility. In addition, the results aim to stimulate ongoing critical debates on this subject.

Generative artificial intelligence (GenAI) has gained significant popularity in recent years. It is being integrated into a variety of sectors for its abilities in content creation, design, research, and many other functionalities. The capacity of GenAI to create new content—ranging from realistic images and videos to text and even computer code—has caught the attention of both the industry and the general public. The rise of publicly available platforms that offer these services has also made GenAI systems widely accessible, contributing to their mainstream appeal and dissemination. This article delves into the transformative potential and inherent challenges of incorporating GenAI into the domain of judicial decision-making. The article provides a critical examination of the legal and ethical implications that arise when GenAI is used in judicial rulings and their underlying rationale. While the adoption of this technology holds the promise of increased efficiency in the courtroom and expanded access to justice, it also introduces concerns regarding bias, interpretability, and accountability, thereby potentially undermining judicial discretion, the rule of law, and the safeguarding of rights. Around the world, judiciaries in different jurisdictions are taking different approaches to the use of GenAI in the courtroom. Through case studies of GenAI use by judges in jurisdictions including Colombia, Mexico, Peru, and India, this article maps out the challenges presented by integrating the technology in judicial determinations, and the risks of embracing it without proper guidelines for mitigating potential harms. Finally, this article develops a framework that promotes a more responsible and equitable use of GenAI in the judiciary, ensuring that the technology serves as a tool to protect rights, reduce risks, and ultimately, augment judicial reasoning and access to justice.

A detailed exploration is presented of the integration of human–machine collaboration in governance and policy decision-making, against the backdrop of increasing reliance on artificial intelligence (AI) and automation. This exploration focuses on the transformative potential of combining human cognitive strengths with machine computational capabilities, particularly emphasizing the varying levels of automation within this collaboration and their interaction with human cognitive biases. Central to the discussion is the concept of dual-process models, namely Type I and II thinking, and how these cognitive processes are influenced by the integration of AI systems in decision-making. An examination of the implications of these biases at different levels of automation is conducted, ranging from systems offering decision support to those operating fully autonomously. Challenges and opportunities presented by human–machine collaboration in governance are reviewed, with a focus on developing strategies to mitigate cognitive biases. Ultimately, a balanced approach to human–machine collaboration in governance is advocated, leveraging the strengths of both humans and machines while consciously addressing their respective limitations. This approach is vital for the development of governance systems that are both technologically advanced and cognitively attuned, leading to more informed and responsible decision-making.

In this article, I will consider the moral issues that might arise from the possibility of creating more complex and sophisticated autonomous intelligent machines or simply artificial intelligence (AI) that would have the human capacity for moral reasoning, judgment, and decision-making, and (the possibility) of humans enhancing their moral capacities beyond what is considered normal for humanity. These two possibilities raise an urgency for ethical principles that could be used to analyze the moral consequences of the intersection of AI and transhumanism. In this article, I deploy personhood-based relational ethics grounded on Afro-communitarianism as an African ethical framework to evaluate some of the moral problems at the intersection of AI and transhumanism. In doing so, I will propose some Afro-ethical principles for research and policy development in AI and transhumanism.

Discussions of the development and governance of data-driven systems have, of late, come to revolve around questions of trust and trustworthiness. However, the connections between them remain relatively understudied and, more importantly, the conditions under which the latter quality of trustworthiness might reliably lead to the placing of ‘well-directed’ trust. In this paper, we argue that this challenge for the creation of ‘rich’ trustworthiness, which we term the Trustworthiness Recognition Problem, can usefully be approached as a problem of effective signalling, and suggest that its resolution can be informed by a multidisciplinary approach that relies on insights from economics and behavioural ecology. We suggest, overall, that the domain specificity inherent to the signalling theory paradigm offers an effective solution to the TRP, which we believe will be foundational to whether and how rapidly improving technologies are integrated in the healthcare space. We suggest that solving the TRP will not be possible without taking an interdisciplinary approach and suggest further avenues of inquiry that we believe will be fruitful.

Improved health data governance is urgently needed due to the increasing use of digital technologies that facilitate the collection of health data and growing demand to use that data in artificial intelligence (AI) models that contribute to improving health outcomes. While most of the discussion around health data governance is focused on policy and regulation, we present a practical perspective. We focus on the context of low-resource government health systems, using first-hand experience of the Zanzibar health system as a specific case study, and examine three aspects of data governance: informed consent, data access and security, and data quality. We discuss the barriers to obtaining meaningful informed consent, highlighting the need for more research to determine how to effectively communicate about data and AI and to design effective consent processes. We then report on the process of introducing data access management and information security guidelines into the Zanzibar health system, demonstrating the gaps in capacity and resources that must be addressed during the implementation of a health data governance policy in a low-resource government system. Finally, we discuss the quality of service delivery data in low-resource health systems such as Zanzibar’s, highlighting that a large quantity of data does not necessarily ensure its suitability for AI development. Poor data quality can be addressed to some extent through improved data governance, but the problem is inextricably linked to the weakness of a health system, and therefore AI-quality data cannot be obtained through technological or data governance measures alone.

The EUMigraTool (EMT) provides short-term and mid-term predictions of asylum seekers arriving in the European Union, drawing on multiple sources of public information and with a focus on human rights. After 3 years of development, it has been tested in real environments by 17 NGOs working with migrants in Spain, Italy, and Greece.

This paper will first describe the functionalities, models, and features of the EMT. It will then analyze the main challenges and limitations of developing a tool for non-profit organizations, focusing on issues such as (1) the validation process and accuracy, and (2) the main ethical concerns, including the challenging exploitation plan when the main target group are NGOs.

The overall purpose of this paper is to share the results and lessons learned from the creation of the EMT, and to reflect on the main elements that need to be considered when developing a predictive tool for assisting NGOs in the field of migration.

In the literature, there are polarized views regarding the capabilities of technology to embed societal values. One aisle of the debate contends that technical artifacts are value-neutral since values are not peculiar to inanimate objects. Scholars on the other side of the aisle argue that technologies tend to be value-laden. With the call to embed ethical values in technology, this article explores how AI and other adjacent technologies are designed and developed to foster social justice. Drawing insights from prior studies, this paper identifies seven African moral values considered central to actualizing social justice; of these, two stand out—respect for diversity and ethnic neutrality. By introducing use case analysis along with the Discovery, Translation, and Verification (DTV) framework and validating via Focus Group Discussion, this study revealed novel findings: first, ethical value analysis is best carried out alongside software system analysis. Second, to embed ethics in technology, interdisciplinary expertise is required. Third, the DTV approach combined with the software engineering methodology provides a promising way to embed moral values in technology. Against this backdrop, the two highlighted ethical values—respect for diversity and ethnic neutrality—help ground the pursuit of social justice.

Nigeria has a significant gender financial inclusion gap with women disproportionately represented among the financially excluded. Artificial intelligence (AI) powered financial technologies (fintech) present distinctive advantages for enhancing women’s inclusion. This includes efficiency gains, reduced transaction costs, and personalized services tailored to women’s needs. Nonetheless, AI harbours a paradox. While it promises to address financial inclusion, it can also inadvertently perpetuate and amplify gender bias. The critical question is thus, how can AI effectively address the challenges of women’s financial exclusion in Nigeria? Using publicly available data, this research undertakes a qualitative analysis of AI-powered Fintech services in Nigeria. Its objective is to understand how innovations in financial services correspond to the needs of potential users like unbanked or underserved women. The research finds that introducing innovative financial services and technology is insufficient to ensure inclusion. Financial inclusion requires the availability, accessibility, affordability, appropriateness, sustainability, and alignment of services with the needs of potential users, and policy-driven strategies that aid inclusion.

In the mid to late 19th century, much of Africa was under colonial rule, with the colonisers exercising power over the labour and territory of Africa. However, as much as Africa has predominantly gained independence from traditional colonial rule, another form of colonial rule still dominates the African landscape. This similitude of these different forms of colonialism is found in the power dominance exhibited by Western technological corporations, just like the traditional colonialists. In this digital age, digital colonialism manifests in Africa through the control and ownership of critical digital infrastructure by foreign entities, leading to unequal data flow and asymmetrical power dynamics. This usually occurs under the guise of foreign corporations providing technological assistance to the continent.

By drawing references from the African continent, this article examines the manifestations of digital colonialism and the factors that aid its occurrence on the continent. It further explores the manifestations of digital colonialism in technologies such as Artificial Intelligence (AI) while analysing the occurrence of data exploitation on the continent and the need for African ownership in cultivating the digital future of the African continent. The paper also recognises the benefits linked to the use of AI and makes a cautious approach toward the deployment of AI tools in Africa. It then concludes by recommending the implementation of laws, regulations, and policies that guarantee the inclusiveness, transparency, and ethical values of new technologies, with strategies toward achieving a decolonised digital future on the African continent.

This paper questions how the drive toward introducing artificial intelligence (AI) in all facets of life might endanger certain African ethical values. It argues in the affirmative that indeed two primary values that are prized in nearly all versions of sub-Saharan African ethics (available in the literature) might sit in direct opposition to the fundamental motivation of corporate adoption of AI; these values are Afro-communitarianism grounded on relationality, and human dignity grounded on a normative conception of personhood. This paper offers a unique perspective on AI ethics from the African place, as there is little to no material in the literature that discusses the implications of AI on African ethical values. The paper is divided into two broad sections that are focused on (i) describing the values at risk from AI and (ii) showing how the current use of AI undermines these said values. In conclusion, I suggest how to prioritize these values in working toward the establishment of an African AI ethics framework.

This article examines the National Health Data Network (RNDS), the platform launched by the Ministry of Health in Brazil as the primary tool for its Digital Health Strategy 2020–2028, including innovation aspects. The analysis is made through two distinct frameworks: Right to health and personal data protection in Brazil. The first approach is rooted in the legal framework shaped by Brazil’s trajectory on health since 1988, marked by the formal acknowledgment of the Right to health and the establishment of the Unified Health System, Brazil’s universal access health system, encompassing public healthcare and public health actions. The second approach stems from the repercussions of the General Data Protection Law, enacted in 2018 and the inclusion of Right to personal data protection in Brazilian’s Constitution. This legislation, akin to the EU’s General Data Protection Regulations, addressed the gap in personal data protection in Brazil and established principles and rules for data processing. The article begins by explanting the two approaches, and then it provides a brief history of health informatics policies in Brazil, leading to the current Digital Health Strategy and the RNDS. Subsequently, it delves into an analysis of the RNDS through the lenses of the two aforementioned approaches. In the final discussion sections, the article attempts to extract lessons from the analyses, particularly in light of ongoing discussions such as the secondary use of data for innovation in the context of different interpretations about innovation policies.