Search Results (25)

The increasing adoption of artificial intelligence algorithms is redefining decision-making across various industries. In the financial sector, where automated credit granting has undergone profound changes, this transformation raises concerns about biases perpetuated or introduced by AI systems. This study investigates the methods used to identify and mitigate biases in AI models applied to credit granting. We conducted a systematic literature review using the IEEE, Scopus, Web of Science, and Science Direct databases, covering the period from 1 January 2013 to 1 October 2024. From the 414 identified articles, 34 were selected for detailed analysis. Most studies are empirical and quantitative, focusing on fairness in outcomes and biases present in datasets. Preprocessing techniques dominated as the approach for bias mitigation, often relying on public academic datasets. Gender and race were the most studied sensitive attributes, with statistical parity being the most commonly used fairness metric. The findings reveal a maturing research landscape that prioritizes fairness in model outcomes and the mitigation of biases embedded in historical data. However, only a quarter of the papers report more than one fairness metric, limiting comparability across approaches. The literature remains largely focused on a narrow set of sensitive attributes, with little attention to intersectionality or alternative sources of bias. Furthermore, no study employed causal inference techniques to identify proxy discrimination. Despite some promising results—where fairness gains exceed 30% with minimal accuracy loss—significant methodological gaps persist, including the lack of standardized metrics, overreliance on legacy data, and insufficient transparency in model pipelines. Future work should prioritize developing advanced bias mitigation methods, exploring sensitive attributes, standardizing fairness metrics, improving model explainability, reducing computational complexity, enhancing synthetic data generation, and addressing the legal and ethical challenges of algorithms. Full article

This paper explores how credit risk management and human capital development sustain financial stability in microfinance institutions. Both qualitative and quantitative research methods allow this study to investigate credit risk management strategies while examining policies for inclusivity plus incentive plans along with debt portfolio selection efficiency. This research emphasizes that financial operations depend on skilled employees who require motivating interventions alongside training programs while developing ethical practices. The research discovers that organizations with strong credit risk management frameworks along with dedicated personnel achieve enhanced financial performances and reduced default incidents. This study confirms that microfinance institutions need both superior risk management along with human resource development systems to achieve sustainable development. This study enriches economic development research by demonstrating that implementing an equal mixture of financial and human resources produces successful economic results. Full article

This article argues that rational self-interest is but one dimension of human behavior. In the context of ‘overshoot’—the excessive consumption of resources beyond the planet’s regenerative capacity—rational self-interest proves detrimental from an evolutionary perspective. This paper provides an alternative to the Tragedy of The Commons, which results from collective action problems when rational, self-interested consumers put their individual welfare over that of the group, and offers the relational consumer, one who balances individual and group interests. Highlighting how ethics play a pivotal role in consumer behavior, we discuss human behavior, resource management, and collective action, then examine these theoretical frameworks in two case studies based in southern Africa. First is a biotic example of The Commons paired with the uMhlathuze Water Stewardship Partnership; second is an abiotic example of The Commons paired with Rotating Savings and Credit Association. These case studies exemplify not only that coordination is the best outcome, but also that institutions promoting self-interested behavior can undermine both collective and individual well-being. Considering intercultural ethics can augment consumer theory, especially in terms of sustainable long-term development. Full article

The widespread adoption of Generative Artificial Intelligence (GenAI) tools in higher education has necessitated the development of appropriate and ethical usage guidelines. This study aims to explore and assess publicly available guidelines covering the use of GenAI tools in universities, following a predefined checklist. We searched and downloaded publicly accessible guidelines on the use of GenAI tools from the websites of the top 50 universities globally, according to the 2025 QS university rankings. From the literature on GenAI use guidelines, we created a 24-item checklist, which was then reviewed by a panel of experts. This checklist was used to assess the characteristics of the retrieved university guidelines. Out of the 50 university websites explored, guidelines were publicly accessible on the sites of 41 institutions. All these guidelines allowed for the use of GenAI tools in academic settings provided that specific instructions detailed in the guidelines were followed. These instructions encompassed securing instructor consent before utilization, identifying appropriate and inappropriate instances for deployment, employing suitable strategies in classroom settings and assessment, appropriately integrating results, acknowledging and crediting GenAI tools, and adhering to data privacy and security measures. However, our study found that only a small number of the retrieved guidelines offered instructions on the AI algorithm (understanding how it works), the documentation of prompts and outputs, AI detection tools, and mechanisms for reporting misconduct. Higher education institutions should develop comprehensive guidelines and policies for the responsible use of GenAI tools. These guidelines must be frequently updated to stay in line with the fast-paced evolution of AI technologies and their applications within the academic sphere. Full article

Artificial intelligence (AI) and big data have impacted different professional sectors in our society. Communication and journalism are clearly among them. From the automatic generation of content to the identification of topics of interest or monitoring of users’ usage habits, AI introduces important training challenges for professionals in the field of communication. Meanwhile, big data analytics enables data journalists to handle large amounts of information in an automated manner, allowing them to perform in-depth analysis of disorganized data. This study analyzes the integration of artificial intelligence (AI) and big data in the curricula of journalism degrees offered by Spanish universities. The research employs quantitative and qualitative methods to examine the typology, syllabus, and distribution of subjects directly or indirectly addressing AI and big data topics, based on indicators such as structure, credit system, objectives, competencies, and professional profiles. The results reveal a scarce integration of AI and Big Data subjects in journalism curricula in Spain. Among the analyzed courses, only seven addressed data journalism as a complete course, while 19 introduced AI and Big Data as part of more general content. The study highlights the need for journalism education to adapt to the disruptive impact of AI and big data on the profession. It discusses the debate between focusing on teaching technological skills versus providing critical and ethical values. The research aims to contribute to the discussion on the readiness of journalism curricula to cope with technological advancements by analyzing the Spanish case. Full article

Credit unions in Latin America play an important role in the financial system, making a significant contribution to the achievement of the Sustainable Development Goals (SDGs) through their focus on financial inclusion, sustainability, and economic resilience. Assessing the social responsibility of these cooperatives ensures ethical, sustainable operations that benefit the population. Unlike traditional financial institutions, cooperatives are based on principles focused on mutual benefit, democratic participation, and responsibility toward their members and the community. This critical literature review, conducted through scientific databases, synthesizes findings on social responsibility in credit unions. The financial system is relevant for global economic stability and growth, comprising institutions like credit unions that facilitate capital flow. It operates through financial instruments, intermediaries, and markets, ensuring efficient resource allocation and risk management. Effective financial management involves planning, organizing, directing, and controlling resources to achieve stability and growth, integrating social responsibility. Credit unions in Latin America highlight cooperative principles, emphasizing member service, community development, and sustainable practices over profit maximization, thereby fostering economic inclusion and ethical business practices. In conclusion, credit unions provide affordable financial services while promoting values of solidarity and equity. However, as entities directly linked to communities, it is essential for them to monitor their actions in terms of social responsibility. This is important to measure and ensure their impact on society and its context. Finally, future research should focus on balancing economic viability with social responsibility, exploring innovative models, governance frameworks, and technological impacts. Full article

This study examines the applications, benefits, challenges, and ethical considerations of artificial intelligence (AI) in the banking and finance sectors. It reviews current AI regulation and governance frameworks to provide insights for stakeholders navigating AI integration. A descriptive analysis based on a literature review of recent research is conducted, exploring AI applications, benefits, challenges, regulations, and relevant theories. This study identifies key trends and suggests future research directions. The major findings include an overview of AI applications, benefits, challenges, and ethical issues in the banking and finance industries. Recommendations are provided to address these challenges and ethical issues, along with examples of existing regulations and strategies for implementing AI governance frameworks within organizations. This paper highlights innovation, regulation, and ethical issues in relation to AI within the banking and finance sectors. Analyzes the previous literature, and suggests strategies for AI governance framework implementation and future research directions. Innovation in the applications of AI integrates with fintech, such as preventing financial crimes, credit risk assessment, customer service, and investment management. These applications improve decision making and enhance the customer experience, particularly in banks. Existing AI regulations and guidelines include those from Hong Kong SAR, the United States, China, the United Kingdom, the European Union, and Singapore. Challenges include data privacy and security, bias and fairness, accountability and transparency, and the skill gap. Therefore, implementing an AI governance framework requires rules and guidelines to address these issues. This paper makes recommendations for policymakers and suggests practical implications in reference to the ASEAN guidelines for AI development at the national and regional levels. Future research directions, a combination of extended UTAUT, change theory, and institutional theory, as well as the critical success factor, can fill the theoretical gap through mixed-method research. In terms of the population gap can be addressed by research undertaken in a nation where fintech services are projected to be less accepted, such as a developing or Islamic country. In summary, this study presents a novel approach using descriptive analysis, offering four main contributions that make this research novel: (1) the applications of AI in the banking and finance industries, (2) the benefits and challenges of AI adoption in these industries, (3) the current AI regulations and governance, and (4) the types of theories relevant for further research. The research findings are expected to contribute to policy and offer practical implications for fintech development in a country. Full article

Joseph Kosuth, one of Concept Art’s influential practitioners, credited Marcel Duchamp in an important 1969 essay, “Art After Philosophy”, with instigating the shift from the visual to the conceptual by means of indifference and dematerialization. Duchamp’s approach to art was not limited, however, to the realm of artistic intention but also included the (re)contextualization provoked by his readymades. This (re)contextualization elucidated the embodied, dialogical encounters of the artist-artwork-audience, what I identify as an “aesthetics of difference”. This designation sets forth a framework of meaning that draws upon a burgeoning subset of early-twentieth-century personalist philosophy called dialogical personalism in order to offer a more suitable plausibility structure than the usual explanations of Duchamp and his approach to art, which typically revolve around nihilism, absurdity, and a solipsistic understanding of freedom. In doing so, Duchamp’s artistic approach retains not only a more viable ontology for continuing to question the nature of art, but also has important epistemological and ethical implications. Full article

The significant advancements in applying artificial intelligence (AI) to healthcare decision-making, medical diagnosis, and other domains have simultaneously raised concerns about the fairness and bias of AI systems. This is particularly critical in areas like healthcare, employment, criminal justice, credit scoring, and increasingly, in generative AI models (GenAI) that produce synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including generative biases that affect the representation of individuals in synthetic data. This survey study offers a succinct, comprehensive overview of fairness and bias in AI, addressing their sources, impacts, and mitigation strategies. We review sources of bias, such as data, algorithm, and human decision biases—highlighting the emergent issue of generative AI bias, where models may reproduce and amplify societal stereotypes. We assess the societal impact of biased AI systems, focusing on perpetuating inequalities and reinforcing harmful stereotypes, especially as generative AI becomes more prevalent in creating content that influences public perception. We explore various proposed mitigation strategies, discuss the ethical considerations of their implementation, and emphasize the need for interdisciplinary collaboration to ensure effectiveness. Through a systematic literature review spanning multiple academic disciplines, we present definitions of AI bias and its different types, including a detailed look at generative AI bias. We discuss the negative impacts of AI bias on individuals and society and provide an overview of current approaches to mitigate AI bias, including data pre-processing, model selection, and post-processing. We emphasize the unique challenges presented by generative AI models and the importance of strategies specifically tailored to address these. Addressing bias in AI requires a holistic approach involving diverse and representative datasets, enhanced transparency and accountability in AI systems, and the exploration of alternative AI paradigms that prioritize fairness and ethical considerations. This survey contributes to the ongoing discussion on developing fair and unbiased AI systems by providing an overview of the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the emerging field of generative AI. Full article

Artificial intelligence (AI) is transforming the mortgage market at every stage of the value chain. In this paper, we examine the potential for the mortgage industry to leverage AI to overcome the historical and systemic barriers to homeownership for members of Black, Brown, and lower-income communities. We begin by proposing societal, ethical, legal, and practical criteria that should be considered in the development and implementation of AI models. Based on this framework, we discuss the applications of AI that are transforming the mortgage market, including digital marketing, the inclusion of non-traditional “big data” in credit scoring algorithms, AI property valuation, and loan underwriting models. We conclude that although the current AI models may reflect the same biases that have existed historically in the mortgage market, opportunities exist for proactive, responsible AI model development designed to remove the systemic barriers to mortgage credit access. Full article

This study explores the adoption and impact of artificial intelligence (AI) and machine learning (ML) in financial markets, utilizing a mixed-methods approach that includes a quantitative survey and a qualitative analysis of existing research papers, reports, and articles. The quantitative results demonstrate the growing adoption of AI and ML technologies in financial institutions and their most common applications, such as algorithmic trading, risk management, fraud detection, credit scoring, and customer service. Additionally, the qualitative analysis identifies key themes, including AI and ML adoption trends, challenges and barriers to adoption, the role of regulation, workforce transformation, and ethical and social considerations. The study highlights the need for financial professionals to adapt their skills and for organizations to address challenges, such as data privacy concerns, regulatory compliance, and ethical considerations. The research contributes to the knowledge on AI and ML in finance, helping policymakers, regulators, and professionals understand their benefits and challenges. Full article

Sustainable financial fraud detection (FD) comprises the use of sustainable and ethical practices in the detection of fraudulent activities in the financial sector. Credit card fraud (CCF) has dramatically increased with the advances in communication technology and e-commerce systems. Recently, deep learning (DL) and machine learning (ML) algorithms have been employed in CCF detection due to their features’ capability of building a powerful tool to find fraudulent transactions. With this motivation, this article focuses on designing an intelligent credit card fraud detection and classification system using the Garra Rufa Fish optimization algorithm with an ensemble-learning (CCFDC-GRFOEL) model. The CCFDC-GRFOEL model determines the presence of fraudulent and non-fraudulent credit card transactions via feature subset selection and an ensemble-learning process. To achieve this, the presented CCFDC-GRFOEL method derives a new GRFO-based feature subset selection (GRFO-FSS) approach for selecting a set of features. An ensemble-learning process, comprising an extreme learning machine (ELM), bidirectional long short-term memory (BiLSTM), and autoencoder (AE), is used for the detection of fraud transactions. Finally, the pelican optimization algorithm (POA) is used for parameter tuning of the three classifiers. The design of the GRFO-based feature selection and POA-based hyperparameter tuning of the ensemble models demonstrates the novelty of the work. The simulation results of the CCFDC-GRFOEL technique are tested on the credit card transaction dataset from the Kaggle repository and the results demonstrate the superiority of the CCFDC-GRFOEL technique over other existing approaches. Full article

The usage of Large Language Models (LMMs) and their exponential progress has created a Cambrian Explosion in the development of new tools for almost every field of science and technology, but also presented significant concerns regarding the AI ethics and creation of sophisticated malware and phishing attacks. Moreover, several worries have arisen in the field of dataset collection and intellectual property in that many datasets may exist without the license of the respective owners. Triple-Entry Accounting (TEA) has been proposed by Ian Grigg to increase transparency, accountability, and security in financial transactions. This method expands upon the traditional double-entry accounting system, which records transactions as debits and credits in two separate ledgers, by incorporating a third ledger as an independent verifier via a digitally signed receipt. The utilization of a digital signature provides evidentiary power to the receipt, thus reducing the accounting problem to one of the presence or absence of the receipt. The integrity issues associated with double-entry accounting can be addressed by allowing the parties involved in the transaction to share the records with an external auditor. This manuscript proposes a novel methodology to apply triple-entry accounting records on a publicly accessed distributed ledger technology medium to control the queries of LLMs in order to discourage malicious acts and ensure intellectual property rights. Full article

Rural credit is one of the most critical inputs for farm production across the globe. Despite so many advances in digitalization in emerging and developing economies, still a large part of society like small farm holders, rural youth, and women farmers are untouched by the mainstream of banking transactions. Machine learning-based technology is giving a new hope to these individuals. However, it is the banking or non-banking institutions that decide how they will adopt this advanced technology, to have reduced human biases in loan decision making. Therefore, the scope of this study is to highlight the various AI-ML- based methods for credit scoring and their gaps currently in practice by banking or non-banking institutions. For this study, systematic literature review methods have been applied; existing research articles have been empirically reviewed with an attempt to identify and compare the best fit AI-ML-based model adopted by various financial institutions worldwide. The main purpose of this study is to present the various ML algorithms highlighted by earlier researchers that could be fit for a credit assessment of rural borrowers, particularly those who have no or inadequate loan history. However, it would be interesting to recognize further how the financial institutions could be able to blend the traditional and digital methods successfully without any ethical challenges. Full article

In recent years, the use of Artificial Intelligence agents to augment and enhance the operational decision making of human agents has increased. This has delivered real benefits in terms of improved service quality, delivery of more personalised services, reduction in processing time, and more efficient allocation of resources, amongst others. However, it has also raised issues which have real-world ethical implications such as recommending different credit outcomes for individuals who have an identical financial profile but different characteristics (e.g., gender, race). The popular press has highlighted several high-profile cases of algorithmic discrimination and the issue has gained traction. While both the fields of ethical decision making and Explainable AI (XAI) have been extensively researched, as yet we are not aware of any studies which have examined the process of ethical decision making with Intelligence augmentation (IA). We aim to address that gap with this study. We amalgamate the literature in both fields of research and propose, but not attempt to validate empirically, propositions and belief statements based on the synthesis of the existing literature, observation, logic, and empirical analogy. We aim to test these propositions in future studies. Full article