A context-driven approach is necessary to translate principles like explainability into practice globally. These vignettes illustrate how AI can be made more trustworthy for users in the Global South through more creative, context-rooted approaches to legibility.
Category: Type: Research
In this category:
1. Peer-Reviewed:
Research Papers
Chapters
Conference Proceedings
2. Pre-Prints. Pre-Prints are standard in some fields. They are not always peer reviewed.
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How Issav Asimov Predicted/Influenced the Present AI Scenario
This paper investigates Isaac Asimov’s impact on modern artificial intelligence (AI) and robotics, focusing on how his visionary narratives and Three Laws of Robotics resonate with current technological practices and ethical debates. Analyzing specific predictions from Asimov’s works that have materialized in today’s AI applications, we draw parallels between his fictional insights and real-world technologies from leading tech firms. The study further considers the social implications of AI, including issues of human displacement and trust. We also discuss the progress and challenges in formulating global ethical standards for AI, reflecting on national and international efforts. The analysis highlights Asimov’s lasting influence and the ongoing importance of ethical deliberation in the AI field.
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Artificial intelligence skepticism in news production: The case of South Africa’s mainstream news organizations
This chapter demonstrates that the celebratory acceptance of artificial intelligence (AI) appropriation, popular in mainstream scholarly discourses of AI, is often colored by an emerging, strong pushback by skeptical journalists. Using the case of South African journalists, we make two broad but related arguments. First, we argue that skepticism about AI among journalists in South Africa should be linked to the broader debates about the future and purpose of journalism in post-apartheid South Africa. Second, we argue that journalists view themselves as a peculiar community with a specific role of serving democracy—a role that will not sync neatly with AI practices. This chapter contributes to debates on AI and news production practices in less-explored global South contexts.
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The Usefulness of Big Data and IoT/AI at Dubai University. Kurdish Studies, 12(2), pp.6198-6220
Digital transformation is disrupting most sectors and most so the education sector. Universities across the world are using technology to reach out to students and to deliver classes remotely enabling students and staff to adopt modern emerging technologies. Dubai University, based in the heart of a technological hub, has the unique opportunity to leverage cutting-edge technologies like Big Data, the Internet of Things (IoT), and Artificial Intelligence (AI) to revolutionize its academic and operational landscape. This research study explores the usefulness of emerging technologies in enhancing Educational Experiences by analyzing Big Data of student learning patterns, engagement levels, and performance to unlock personalized learning pathways, adaptive courseware, and targeted interventions. AI-powered tutoring systems and virtual labs offer immersive and customized learning experiences shortly. IoT sensors can monitor and manage energy consumption, building security, and resource allocation, leading to sustainable and efficient campus operations. AI-powered systems can automate administrative tasks, streamline processes, and provide predictive maintenance for facilities. The main contribution of the study is using PLS-SEM modeling to analyze Big Data enabling researchers to extract insights from vast datasets and make data-driven discoveries. AI-powered tools can aid in research design, data analysis, and scientific simulation, fostering a culture of innovation. This study will employ a mixed-method approach, utilizing quantitative data analysis of existing university data sets and qualitative interviews with stakeholders. The findings will contribute to developing a strategic roadmap for the optimal integration of Big Data, IoT, and AI within Dubai University’s ecosystem. This research aims to position Dubai University as a pioneer in education and innovation, setting a benchmark for higher education institutions in the region and beyond. The study aims to provide insights to empower decision-makers at Dubai University to make well-informed choices regarding the adoption and integration of emerging technologies. The study facilitates strategic planning by comprehensively grasping the challenges and opportunities presented by digital transformation. Moreover, it guides resource allocation and offers recommendations for leveraging data analytics to support students who may be at risk.
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fMRIflows: A Consortium of Fully Automatic Univariate and Multivariate fMRI Processing Pipelines
One of the most significant current discussions in the understanding of the human brain is the functional recruitment of some regions of the cortex for specific tasks, regardless of the sensory modality (e.g. visual, tactile or auditory) in which the stimuli is received. The ability to perceive motion, among other visual properties, is a fundamental faculty of the human brain. Brain lesions that impair the detection and processing of motion have a profound impact on daily activity. Consequently, visual motion processing is one of the most fundamental and well-studied systems in the human brain, canonically known to develop mainly for the purpose of visual perception. A great deal of study on the multisensory responses to motion processing in the human brain focused on the middle temporal complex and superior temporal sulcus. Several studies using both neurophysiological and neuroimaging techniques showed the multisensory properties of these areas, showing their recruitment during both tactile and auditory motion stimulation. Despite the large amount of study on the topic it is still unclear whether the recruitment of these areas directly mediates the perception of motion through the different sensory input or regulates responses within primary sensory areas involved in the task. This MSCA fellowship allowed me to lay the foundations on the neural substrate underlying multisensory motion perception. We discovered that hMT+, an area mainly involved in visual motion processing, encode motion via spatial features of the stimulation rather than its intrinsic speed and our preliminary results show that, together with other visual areas, is able to decode speed via auditory and tactile motion stimulation, proving its multisensory function.
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Strengthening Data Protection: Ensuring Privacy and Security for Nigerian Citizens
This Policy Brief examines the existing data protection regime both in Nigeria and globally and suggests ways to improve the data protection efforts in Nigeria. It considers Nigeria’s principal data protection laws, generally applicable across all sectors (including public and private institutions). By examining and juxtaposing some of the exemptions in legislation, an opportunity for abuse of data subjects’ rights may have been inadvertently created by laws that were enacted to do otherwise. This Policy Brief proffers preferable outcomes that may guide engagement with policymakers to rectify this situation.
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BrainStat: A toolbox for brain-wide statistics and multimodal feature associations
Analysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly on associations with respect to other brain-derived features such as gene expression, histological data, and functional as well as cognitive architectures. Here, we introduce BrainStat – a toolbox for (i) univariate and multivariate linear models in volumetric and surface-based brain imaging datasets, and (ii) multidomain feature association of results with respect to spatial maps of post-mortem gene expression and histology, task-based fMRI meta-analysis, as well as resting-state fMRI motifs across several common surface templates. The combination of statistics and feature associations into a turnkey toolbox streamlines analytical processes and accelerates cross-modal research. The toolbox is implemented in both Python and MATLAB, two widely used programming languages in the neuroimaging and neuroinformatics communities. BrainStat is openly available and complemented by an expandable documentation.
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Data journalism and investigative news reporting practices during the pandemic: The case of Zimbabwe and South Africa
This chapter interrogates the opportunities and challenges provided by data journalism to investigative journalists during the pandemic. Our findings reveal a paradoxical contribution of data journalism to investigative journalism. On the one hand, unprepared newsrooms and journalists found it hard to understand the practice, whose demands were “foreign” to some journalists. Yet on the other hand, data-driven journalism provided immense opportunities to investigative journalists to play their monitory role more effectively – holding the ruling elites to account, providing “lively and real-time” fact-based news on the pandemic, countering state propaganda on the pandemic and widening investigative journalists’ news sourcing routines.
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Unveiling AI Concerns for Sub-Saharan Africa and its Vulnerable Groups
In Sub-Saharan Africa (SSA), artificial intelligence is still in its early stages of adoption. To ensure that the already existing class imbalance in SSA communities does not hinder the realization of the Sustainable Development Goals, such as data security, safety, and equitable access to AI technologies, acceptable reliability measures must be put in place (as policies). This paper identifies some of the vulnerabilities in AI and adds a voice to the risks and ethical concerns surrounding the use of AI and its impact on SSA and its vulnerable groups. Our systematic literature review of related research between January 2014 and June 2024 shows the current state of AI adoption in SSA and the socio-political challenges that impact its development, revealing key concerns in data Governance, safety privacy, educational and skill gaps, socioeconomic impacts, and stakeholder influence on AI adoption in SSA. We propose a framework for designing data governance policies for the inclusive use of AI in SSA.
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Do They Really Care about Us? On the Limits of State Intervention
This paper examines the limits of state intervention through the relationship between freedom and equality, the rule of law and social justice, as well as through two highly contradictory concepts regarding the scope of government action – the concepts of minimal state and paternalistic state. Accordingly, we seek to identify a model capable of outlining the extent to which the state can intervene in the light of socially beneficial goals, but without compromising individual freedom. Since we cannot find such a model within the extreme positions of liberalism and socialism, this paper seeks to offer a satisfactory solution by mitigating some of the ideologically exclusive positions. It embraces Aristotle’s teaching about the middle as a virtue and proposes sophisticated neoliberalism as a potential alternative to the status quo. Still, as insisted, the government should never be allowed to assume uncontrollable powers and create conditions for collectivist doctrines that recognize no individual freedom.
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Artificial intelligence practices in everyday news production: The case of South Africa’s mainstream newsrooms
This article explores artificial intelligence (AI) uptake in selected South African mainstream newsrooms. It seeks to determine the extent to which AI has been adopted and how journalists and editors perceive its appropriation in newsmaking practices. To address these two broad aims, the study used in-depth interviews with journalists and editors. Our findings suggest a slow, varied but methodical uptake of AI practices in South Africa’s mainstream newsrooms. We deduced three uses of AI in these newsrooms. The first is what we call the holistic appropriation of AI. The second one is the exclusively technological appropriation of AI, and the last one is the task-specific appropriation of AI. This varied uptake of AI is taking place against a deep-seated skepticism with this technology.
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Using Science to Support and Develop Employees in the Tech Workforce—An Opportunity for Multidisciplinary Pursuits in Engineering Education
The majority of students who choose to major in engineering do so to become a part of the community of practice of professional engineers (Johri & Olds, 2011), meaning that they want to have adequate exposure to what a career as a professional engineer could potentially be as part of their college experience. However, according to Jonassen (2014), engineering graduates are not well trained to contribute to the workplace due to the complexities associated with engineering work. Stevens, Johri, and O’Connor (2014) described engineering work as that which involves complexity, ambiguity, and contradictions. Since developing the skills for innovation involves analysis of complex, ambiguous, ill-defined, real-world problems (Daly, Mosyjowski, & Seifert, 2014; Newell, 2010), students must have an opportunity to, at the very least, be exposed to multidisciplinary teams. This emphasis on the need for exposure to multi-disciplinary problem solving holds true not only for undergraduate engineers in training, but also for graduate students focused on engineering education.