Ikpenmosa Uhumuavbi

The description of a combination of technologies as ‘artificial intelligence’ (AI) is misleading. To ascribe intelligence to a statistical model without human attribution points towards an attempt at shifting legal, social, and ethical responsibilities to machines. This paper exposes the deeply flawed characterisation of AI and the unearned assumptions that are central to its current definition, characterisation, and efforts at controlling it. The contradictions in the framing of AI have been the bane of the incapacity to regulate it. A revival of applied definitional framing of AI across disciplines have produced a plethora of conceptions and inconclusiveness. Therefore, the research advances this position with two fundamental and interrelated arguments. First, the difficulty in regulating AI is tied to it characterisation as artificial intelligence. This has triggered existing and new conflicting notions of the meaning of ‘artificial’ and ‘intelligence’, which are broad and largely unsettled. Second, difficulties in developing a global consensus on responsible AI stem from this inconclusiveness. To advance these arguments, this paper utilises functional contextualism to analyse the fundamental nature and architecture of artificial intelligence and human intelligence. There is a need to establish a test for ‘artificial intelligence’ in order ensure appropriate allocation of rights, duties, and responsibilities. Therefore, this research proposes, develops, and recommends an adaptive three-elements, three-step threshold for achieving responsible artificial intelligence.

The notion that multiple legal systems autonomously coexist in independent states in sub-Saharan Africa due to the interaction of the continent with foreign legal systems is theoretically flawed, misleading and an incorrect application of the concept of legal pluralism. This article examines the nature of legal pluralism in the context of transitioning independent nation-states. It contends that these nation-states, especially in sub-Saharan Africa, do not operate legally pluralistic legal systems, as often presented in scholastic works. Using system and lifecycle theories, this article argues against the prevailing conceptualization of legal pluralism in African legal systems. We argue that the prevailing conceptualization of pluralistic legal systems in sub-Saharan Africa is based on the fallacy of the undistributed middle, leading to flawed conclusions and disastrous governance outcomes. This widespread orthodoxy in legal and sociopolitical scholarship heightens perceived conflicts between imperial universalism and cultural existentialism, which has been the epicenter of legal discussion in Africa’s transitioning nations.

The 2030 Development Agenda styled" Transforming our world The 2030 Agenda for Sustainable Development" was hugged by the transnational locales of the UN General Assembly in 2015. Monitoring the progress of countries towards achieving these pretensions is pivotal for sustainable development. This exploration paper offers an innovative stance toward foretelling the SDG Index of Indian states for the near future times using machine learning ways, logical and visualization tools. The paper focuses on India's sweats towards achieving the SDGs and investigates the factors impacting the SDG performance of individual Indians states . A comprehensive dataset is collected, encompassing a wide range of socio-profitable pointers, demographic data, and environmental criteria applicable to each SDG target. Literal SDG Index scores and corresponding state-specific data are collected to assay and find some trends. The study demonstrates the eventuality of vaticination ways in vaticinating the unborn SDG Index scores of Indian states . The time series graph showcases varying degrees of delicacy across different SDGs , indicating the complexity and diversity of experimental challenges.

In any nation, some parts of society are unsatisfied with the idea of paying taxes. Some authorities have tried to fill the gaps in law by adopting techniques to discourage tax evasion and tax fraud. In this case, technology, especially big data have been used to enable tax collection and regulation. This paper studied the pivotal role of big data in rental tax evasion management in different countries. In order to solve the issues, this paper investigates tax evasion in the rental era and then applies the GIS approach in describing geo-behaviours, social connections, and the interaction between taxpayers and properties. The idea is to reduce evasion and fraud in Tax Management in Iran by applying the ‘National Licensing Schema’ for landlords, using Tax Profiling System combined with GIS and GraphDB. This is to identify the landlord’s and tenants’ relationship.

A significant aspect of colour prediction is the process of detecting a colour palette that represents a collection at a fashion show. People do this manually, but often with too many images, it becomes a hard task. An automated machine-learning method has been developed to generate colour palettes for fashion shows. the model was obtained by fashion pictures dataset, representing each of 48 images of her from a particular fashion show. This work can be extended to analyse millions of images from social media feeds and provide data driven insights for colour prediction.

This paper aims to find the boost model which brings the best accuracy in text classification by using Support Vector Machine in comparison with other models namely Naive Bayes, Random Forest Decision Tree and K-NN. For the text classification and processing, the planned system will have to apply with the Support Vector Machine and the result is decided by major roles. Based on the Machine Learning algorithms used for the implementation of the research- the BBC news dataset- illustrates that the Support Vector Machine has better accuracy and result.

Signaling the fundamental tensions in the conceptualization of corporate social responsibility (CSR) and corruption has simply lost its capacity to inspire. Like an emperor without clothes, both concepts are estranged from comprehension. This paper therefore examines these deeply contested conceptions of corruption and CSR frameworks as they relate to Nigeria’s oil-rich Niger Delta. It seeks to test the competing notions within institutional and operational corruption on the one hand and CSR frameworks on the other hand. The idea is to establish a fundamental nexus between the inconsistent narrative conception of the above forms of corruption and the incoherent framing of CSR within institutional settings in Nigeria. This paper maintains the view against the voluntarist conception that sees corruption as the offshoot of cultural disposition wrapped into the logical frames of CSR. As a result, the study seeks to resolve the question of whether corruption is incidental to or a function of framework and systems design. The aspects of relativist, nonrelativist, and communalist analytical methods provide a context for an examination of the competing notions of corruption and its relationship with the incoherent CSR framework in Nigeria’s oil and gas sector. It argues that the intentionality of gaps created within the CSR framework provides the basis for corrupt activities. Initial findings reveal a strong connection between defective systems design and a high tendency for institutional and operational corruption within the CSR framework in Nigeria’s oil and gas sector. This has implications for associated and connected institutional systems in Nigeria, Africa, and across the world.