ARTIFICIAL INTELLIGENCE

Technology has advanced recently, and innovations such as blockchain, artificial intelligence (AI), and data analytics are predicted to have an impact on the auditing and accounting profession. The term "auditing" usually refers to the impartial review and assessment of a business's financial statements, which is often carried out by a third party called an auditor. According to auditor regulations, the auditor's duties include verifying that the financial statements submitted present a true and fair view and offering their opinions on the financial report. They must also provide their assessment of the firm's sustainability within a year of the audited report's date (Jeong, & Rho, 2004). Although the audit process has evolved since its inception, auditors still have to manually take samples of data and use that data to test the validity of the company’s financial statement which can be costly, draining and time-consuming. This method has also proved to be inefficient in this modern age of technology, giving room for manipulation of figures or a misleading audit report. In the past there have been cases of financial crises involving some large organizations such as Enron, WorldCom, and other elite businesses which have brought about some concerns regarding the quality of audit. Since many users of audited financial statements have different expectations of the audit function, the aftermath of these scandals has led to the identification of a perceived expectation gap in audit quality. This has resulted in a call for changes to the auditing profession in order to ensure improved audit quality (Kida, 1980). The world is 11 changing rapidly due to the presence of artificial intelligence (AI), and the field of auditing is no exception. Artificial Intelligence (AI) is defined as “the ability of a computer or machine to mimic intelligent human behaviour” and includes a broad range of methods such as robotics, computer vision, machine learning, and natural language processing (Russell & Norvig, 2016). Research is required to fully understand the promises and challenges of this revolutionary technology, which is upending established auditing processes. Artificial Intelligence has a wide-ranging and continuously growing impact on auditing. Some of the tasks for which AI-powered tools are being used for include; Data analysis, fraud detection, continuous auditing, and audit planning and reporting

The study investigated the ethical and environmental, social, and governance implications of generative artificial intelligence on sustainability practices in Nigeria. The rapid adoption of generative AI has created both opportunities and concerns for organisations striving to enhance sustainable development. The study examined how ethical considerations, environmental responsibility, social impact, and governance practices influence the effective integration of generative AI into sustainability initiatives. A descriptive survey design was adopted. Data were collected from 120 respondents who met the benchmark criteria related to AI, sustainability, and governance. A structured questionnaire was used to assess ethical considerations, environmental outcomes, social effects, governance structures, and sustainability practices. Data were analysed using descriptive statistics, correlation analysis, variance inflation factors, heteroskedasticity diagnostics, and multiple regression at the 5 percent significance level. The findings revealed that ethical considerations significantly improved sustainability practices. Environmental impact demonstrated a meaningful positive influence, indicating that AI-enabled environmental optimisation contributes to sustainability. Social impact also enhanced sustainability practices through inclusiveness, trust building, and knowledge improvement. Governance practices exerted a strong positive effect, showing that oversight, policy compliance, and responsible AI governance are essential for achieving sustainable outcomes. Together, the predictors explained 57.2 percent of the variation in sustainability practices. The study concludes that responsible generative AI adoption depends on ethical values, environmental responsibility, social inclusion, and strong governance structures. Organisations can only achieve sustainable outcomes when AI systems are developed and deployed within these guiding dimensions. The study recommends strengthening ethical frameworks, improving environmental safeguards, promoting socially responsible AI practices, and enhancing governance structures to support sustainable AI integration in Nigeria.

The place of artificial intelligence in cultural pressure and human value has become a source of concern in our contemporary times. Cultural value remains one of the critical aspects of human development. Some scholars have argued that culture in contemporary human society serves as a moral teacher to the people. The purpose of this study is to critically examine the contemporary significance of artificial intelligence on cultural pressure and human value. The place of artificial intelligence in cultural pressure and human value has contemporary significance and very serious ethical implications. The concept of artificial intelligence is predicated on human creativity and it borders on human welfare. A conceptual clarification of artificial intelligence showcases the fact that it is a humanist philosophy. The objective of this research is that artificial intelligence showcases human prosperity. This research work is to take a cursory look at the contemporary influence of artificial intelligence on cultural development and human value. The study covers the ethical implications of artificial intelligence on cultural development and human value. Some human values have been eroded due to the emergence of artificial intelligence.

This study examined the perception of education students at the University of Benin regarding the usage of Artificial Intelligence (AI) tools in their academic activities. The study adopted a descriptive survey research design to gather data from 400 undergraduate students in the Faculty of Education during the 2024/2025 academic session. The instrument used for data collection was a structured questionnaire titled Perception of Education Students on the Usage of Artificial Intelligence Questionnaire (PESUAIQ), validated by experts and tested for reliability using Cronbach’s Alpha. Data were analyzed using descriptive statistics such as mean and standard deviation. The study found that students view AI positively and find it helpful for research, writing, presentations, and accessing information. However, they worry about overdependence, ethical issues, and reduced critical thinking. Poor internet, limited devices, and low digital literacy also affect effective use. Despite this, students remain optimistic about AI’s impact on learning. The study recommends improving digital infrastructure, teaching AI literacy, and creating clear guidelines to support responsible and effective AI use in education

This research presents a comprehensive systematic review of artificial intelligence (AI) techniques and algorithms employed in predictive analytics for drilling hazard management, specifically focusing on stuck pipe incidents, lost circulation events, and wellbore instability. Drilling hazards collectively account for 30-40% of non productive time (NPT) in global drilling operations, costing the oil and gas industry approximately $8-12 billion annually. Traditional monitoring systems rely on reactive, empirical approaches that fail to provide early warnings, while modern drilling operations generate 1-2 terabytes of data per well, creating opportunities for AI-based predictive solutions. Through systematic analysis of 78 peer-viewed research papers published between 2010-2024, this study evaluates the performance characteristics, implementation challenges, and economic viability of various AI algorithms including artificial neural networks (ANNs), support vector machines (SVMs), decision trees, ensemble methods, and deep learning approaches. The research reveals a clear performance hierarchy among AI methods, with deep learning achieving the highest accuracy rates (90-97%) but requiring substantial computational resources and datasets exceeding 50,000 examples. Traditional neural networks demonstrate optimal balance between performance (88-94% accuracy) and practicality, making them the most widely adopted approach in commercial implementations.

This study examined the socio-economic effects of artificial intelligence in Agricultural by farmers and extension agents in Edo state, Nigeria. A stratified sampling procedure was used to select 131 farmers and 82 extension agents that were used for this study. The specific objectives were analyzed using descriptive statistics ( frequency, percentage and mean rating ) why regression analysis was used to analyze the hypothesis. Findings revealed that the majority of farmers (62.4%) and extension agents (61.7%) were male, and most fell within the economically active age range of 31–50 years. Awareness level of AI technologies was high, 74.4% and 91.7% of farmers and extension agents respectively, satellite imagery (64.0% of farmers; 75.0% of extension agents), and climate precision models (60.0% of farmers; 81.7% of extension agents). However, advanced technologies such as remote sensing recorded low adoption (8.0% of farmers; 1.7% of extension agents). Adoption levels of Artificial intelligence(AI) varied and showed mixed sociocultural reactions towards AI technologies. The regression analysis shows that most socio-economic characteristics, sex (β = 0.162; p = 0.020), association membership (β = 0.258; p < 0.01), awareness of AI (β = 0.585; p < 0.01), and location (p = 0.058) of respondents have significant influence on awareness ( β = 0.585; p < 0.01 ) and adoption (Adjusted R² = 0.523 ) of AI technologies with some sociocul tural effects. The study concludes that while awareness and partial adoption of AI technologies are increasing, full integration into agricultural practice is hindered by sociocultural beliefs, limited infrastructure, and gaps in technical capacity

The significance of ethics in Artificial Intelligence (AI) can not be overstated, as it encompasses the foundational principles guiding the responsible creation, deployment and management of AI technologies. As AI systems increasingly permeates every aspect of our lives from healthcare and education to security and entertainment, their decisions and actions have profound implications not only on individual rights and privacy but also on societal norms and values. Ethical considerations in AI are paramount to ensure that these technologies enhance human well-being, uphold fairness rather than perpetuate biases, exacerbate inequalities or undermine democratic institutions. The importance of AI ethics lies in its ability to provide a framework for navigating the complex moral dilemma presented by AI, such as balance between innovation and regulation, the protection of individual privacy versus the benefits of big data and the protection of AI misuse. This project explores AI decision making from an ethical perspective, examining issues such as bias, accountability, transparency and fairness. Through case studies and theoretical analysis, it evaluates how AI systems navigate morally complex situations and the extent to which they align with human ethical principles. This study also discusses existing ethical framework such as Unitarianism, deontology and virtue ethics. Ultimately, the goal is to highlight the need for responsible AI development and governance to ensure that AI driven decisions uphold ethical standards and benefits society as a whole.