Understanding and Mitigating Bias in Imaging Artificial Intelligence.

Increasing concerns have been raised regarding artificial intelligence (AI) bias, and in response, efforts have been made to pursue AI fairness. In this paper, we argue that the idea of structural injustice serves as a helpful framework for clarifying the ethical concerns surrounding AI bias—including the nature of its moral problem and the responsibility for addressing it—and reconceptualizing the approach to pursuing AI fairness. Using AI in health care as a case study, we argue that AI bias is a form of structural injustice that exists when AI systems interact with other social factors to exacerbate existing social inequalities, making some groups of people more vulnerable to undeserved burdens while conferring unearned benefits to others. The goal of AI fairness, understood this way, is to pursue a more just social structure with the development and use of AI systems when appropriate. We further argue that all participating agents in the unjust social structure associated with AI bias bear a shared responsibility to join collective action with the goal of reforming the social structure, and we provide a list of practical recommendations for agents in various social positions to contribute to this collective action.

The rapid deployment of Artificial Intelligence (AI) in Anti-Money Laundering (AML) practices within the financial industry presents significant ethical and governance challenges that must be navigated effectively. As financial institutions increasingly adopt AI technologies to enhance their AML efforts, concerns regarding data privacy, algorithmic bias, and transparency emerge. This review explores the ethical implications of AI in AML and offers governance strategies to mitigate risks while ensuring compliance with regulatory frameworks. One of the primary ethical challenges in deploying AI for AML is the potential for algorithmic bias. AI systems trained on historical data may inadvertently perpetuate existing biases, leading to discriminatory practices in transaction monitoring and customer profiling. This raises serious concerns about fairness and equity in the financial sector. Addressing algorithmic bias requires the implementation of rigorous testing and validation processes to ensure AI systems function impartially across diverse populations. Data privacy is another critical issue. The extensive data collection required for effective AML monitoring raises questions about the protection of sensitive customer information. Financial institutions must establish robust data governance frameworks that prioritize privacy and comply with regulations such as the General Data Protection Regulation (GDPR). Ensuring transparency in how data is used and providing clear communication to customers about data practices is essential for building trust. Effective governance frameworks are crucial in navigating these ethical challenges. Financial institutions should adopt a multi-disciplinary approach that includes ethical guidelines, compliance measures, and risk management strategies. Establishing oversight committees can help ensure that AI deployment aligns with ethical standards and regulatory requirements. Furthermore, ongoing training for employees on the ethical use of AI in AML can foster a culture of responsibility and accountability. This review highlights the need for a balanced approach to AI deployment in AML, emphasizing the importance of ethical considerations and governance structures. As the financial industry continues to evolve, addressing these challenges will be essential for maintaining trust, ensuring compliance, and leveraging AI’s potential to enhance AML practices effectively.

This chapter explores the practical applications of artificial intelligence (AI) tools in enhancing business efficiency, demonstrating how AI-driven solutions can streamline operations, optimize decision-making, and foster innovation across various industries. It provides a structured guide to understanding AI-powered business applications, including machine learning algorithms, predictive analytics, automation tools, and AI-driven customer engagement systems. Through real-world case studies, the chapter highlights how businesses of all sizes—from small startups to large corporations—can leverage AI to reduce costs, improve productivity, and gain competitive advantages. The discussion includes scalable AI solutions, debunking the myth that AI is only accessible to large enterprises. The chapter also examines sector-specific AI applications, showcasing AI’s impact in finance, healthcare, marketing, retail, manufacturing, and logistics. Additionally, this chapter addresses the ethical and societal implications of AI adoption, including privacy concerns, algorithmic bias, workforce transformation, and regulatory considerations. It provides a practical roadmap for AI implementation, guiding business owners in identifying the right AI tools, overcoming challenges, and ensuring responsible AI integration. By the end of this chapter, readers will have a clear understanding of the AI landscape, including how to strategically implement AI tools to maximize business efficiency while maintaining ethical integrity.

he rapid progress in implementing Artificial Intelligence (AI) across various domains such as healthcare decision-making, medical diagnosis, and others has raised significant concerns regarding the fairness and bias embedded within AI systems. This is particularly crucial in sectors like healthcare, employment, criminal justice, credit scoring, and the emerging field of generative AI models (GenAI) producing synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including biases ingrained in the synthetic data representation of individuals.This survey paper provides a concise yet comprehensive examination of fairness and bias in AI, encompassing their origins, ramifications, and potential mitigation strategies. We scrutinize sources of bias, including data, algorithmic, and human decision biases, shedding light on the emergent issue of generative AI bias where models may replicate and amplify societal stereotypes. Assessing the societal impact of biased AI systems, we spotlight the perpetuation of inequalities and the reinforcement of harmful stereotypes, especially as generative AI gains traction in shaping public perception through generated content.Various proposed mitigation strategies are explored, with an emphasis on the ethical considerations surrounding their implementation. We stress the necessity of interdisciplinary collaboration to ensure the effectiveness of these strategies. Through a systematic literature review spanning multiple academic disciplines, we define AI bias and its various types, delving into the nuances of generative AI bias. We discuss the adverse effects of AI bias on individuals and society, providing an overview of current approaches to mitigate bias, including data preprocessing, model selection, and post-processing. Unique challenges posed by generative AI models are highlighted, underscoring the importance of tailored strategies to address them effectively.Addressing bias in AI necessitates a holistic approach, involving diverse and representative datasets, enhanced transparency, and accountability in AI systems, and exploration of alternative AI paradigms prioritizing fairness and ethical considerations. This survey contributes to the ongoing discourse on developing fair and unbiased AI systems by outlining the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the burgeoning field of generative AI.

As new artificial intelligence (AI) tools are being developed and as AI continues to revolutionize healthcare, its potential to advance health equity is increasingly recognized. The 2024 Research Centers in Minority Institutions (RCMI) Consortium National Conference session titled "Artificial Intelligence: Safely, Ethically, and Responsibly" brought together experts from diverse institutions to explore AI's role and challenges in advancing health equity. This report summarizes presentations and discussions from the conference focused on AI's potential and its challenges, particularly algorithmic bias, transparency, and the under-representation of minority groups in AI datasets. Key topics included AI's predictive and generative capabilities in healthcare, ethical governance, and key national initiatives, like AIM-AHEAD. The session highlighted the critical role of RCMI institutions in fostering diverse AI/machine learning research and in developing culturally competent AI tools. Other discussions included AI's capacity to improve patient outcomes, especially for underserved communities, and underscored the necessity for robust ethical standards, a diverse AI and scientific workforce, transparency, and inclusive data practices. The engagement of RCMI institutions is critical to ensure practices in AI development and deployment which prioritize health equity, thus paving the way for a more inclusive AI-driven healthcare system.

This study presents results of a systematic literature review (SLR) of the responsible use of artificial intelligence (AI) tools in higher education, identify patterns of ethical and irresponsible use, and propose a conceptual framework for predicting ethical AI adoption. Following PRISMA guidelines, was conducted on 60 peer-reviewed studies published between 2022 and 2026, sourced from Google Scholar. Studies were mapped against four research questions addressing AI tools used, their applications, reported unethical practices, and predictive modelling approaches. Results reveal that general AI, generative AI tools, and large language models dominate higher education contexts, primarily deployed for personalized learning, academic work, and teaching. Irresponsible practices were documented in one-third of studies, including academic integrity breaches (13.33%), algorithmic bias, and privacy violations. Critically, no existing study developed a real-time predictive model capable of monitoring ethical AI use, despite four studies demonstrating predictive modelling capabilities for other purposes. This study addresses a significant gap by proposing a novel conceptual framework that integrates AI tool deployment, user behaviour, governance measures, and predictive analytics to forecast ethical outcomes. The framework provides higher education institutions with a pathway toward data-informed, proactive governance of AI technologies.

Purpose Algorithm bias refers to repetitive computer program errors that give some users more weight than others. The aim of this article is to provide a deeper insight of algorithm bias in AI-enabled ERP software customization. Although algorithmic bias in machine learning models has uneven, unfair and unjust impacts, research on it is mostly anecdotal and scattered. Design/methodology/approach As guided by the previous research (Akter et al., 2022), this study presents the possible design bias (model, data and method) one may experience with enterprise resource planning (ERP) software customization algorithm. This study then presents the artificial intelligence (AI) version of ERP customization algorithm using k-nearest neighbours algorithm. Findings This study illustrates the possible bias when the prioritized requirements customization estimation (PRCE) algorithm available in the ERP literature is executed without any AI. Then, the authors present their newly developed AI version of the PRCE algorithm that uses ML techniques. The authors then discuss its adjoining algorithmic bias with an illustration. Further, the authors also draw a roadmap for managing algorithmic bias during ERP customization in practice. Originality/value To the best of the authors’ knowledge, no prior research has attempted to understand the algorithmic bias that occurs during the execution of the ERP customization algorithm (with or without AI).

The rise of artificial intelligence (AI), particularly ChatGPT, has transformed educational landscapes globally. Moreover, the Beijing Consensus on Artificial Intelligence and Education and the ‘Pact for the Future’ propose that AI can support UNESCO in achieving development goals, especially focusing on SDG 4, which emphasizes quality education. Thus, this study investigates undergraduate students’ familiarity with and attitudes toward AI tools, as well as their perceived risks and benefits of using AI tools at a private university in China. An explanatory sequential mixed-method design was employed with an online survey of 167 students, followed by a qualitative analysis of open-ended responses. Data were analyzed using the one-sample Wilcoxon signed-rank test and thematic analysis, supported by SPSS and ATLAS.ti 25. The findings revealed that students demonstrated moderate familiarity with AI tools, particularly ChatGPT and willingness to use them in coursework. Positive attitudes toward AI’s value in education were evident, although concerns such as dependence and reduced independent thinking, algorithmic bias and ethical concerns, accuracy and information quality, data security, and privacy concerns were observed among students. Moreover, students generally viewed AI positively and perceived AI integration as inevitable and becoming common in academic settings. Students were concerned that the misuse of AI by their teachers was minimal and trusted their teachers to use AI effectively in teaching. Students also perceived AI’s benefits, such as personalized learning, efficiency and convenience, career and skill development, and support for independent learning. This study contributes to the discourse on AI integration in higher education by highlighting students’ nuanced perceptions and balancing their benefits with potential risks. The findings of this study were limited by the small sample size and institution. Future research should explore diverse contexts to develop comprehensive AI implementation frameworks for higher education.

Artificial intelligence (AI) has become an integral part of modern life, impacting various sectors. While it offers significant benefits, AI also presents safety, ethical and social challenges. This literature review examines these challenges, focusing on safety of data privacy, algorithmic bias, decision reliability, and workforce displacement due to automation. The widespread use of AI requires robust data protection measures to safeguard personal information from breaches and misuse. Algorithmic bias, often unintentional, can lead to discriminatory outcomes. The lack of transparency in some AI systems raises concerns about the trustworthiness of their decisions. Furthermore, AI-driven automation has the potential to displace workers, requiring careful planning and strategies for workforce transition. This review explores the complex interplay between AI's potential and its risks. It examines the ethical implications of data collection and usage, the challenges of ensuring algorithmic fairness, and the socioeconomic impact of automation. Finally, it proposes strategies for responsible AI development and deployment, aiming to maximize benefits while mitigating potential harms. These strategies include promoting AI literacy, developing transparent and accountable AI systems, and fostering a balanced approach to AI adoption.

Algorithmic bias in artificial intelligence (AI) is a growing concern, especially in the employment sector, where it can have devastating effects on both individuals and society. Gender discrimination is one of the most prevalent forms of algorithmic bias seen in numerous industries, including technology. The underrepresentation of women in the field of information technology is a well-known issue, and several organizations have made tackling this issue a top priority. Amazon, one of the world's top technology businesses, has been at the forefront of initiatives to increase inclusiveness and diversity in the sector. Concerns exist, however, that algorithmic bias in their recruitment process may perpetuate discrimination based on gender. This study intends to investigate these issues by employing an interpretive epistemology and utilizing interviews and focus groups to acquire a more nuanced knowledge of the subject,with keyfactors contributing to algorithmic gender bias in Amazon's recruitmentprocess andrecommend strategies for improving women's employment in information technology.

A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry.

This study delves into the ethical implications of Artificial Intelligence (AI) in financial decision-making, exploring the transformative impact of AI technologies on the financial services sector. Through a comprehensive literature review, the research highlights the dual nature of AI's integration into finance, showcasing both its potential to enhance operational efficiency and decision accuracy and the ethical challenges it introduces. These challenges include concerns over data privacy, algorithmic bias, and the potential for systemic risks, underscoring the need for robust ethical frameworks and regulatory standards. The study emphasizes the importance of a multidisciplinary approach to AI development and deployment, advocating for collaboration among technologists, ethicists, policymakers, and end-users to ensure that AI technologies are aligned with societal values and ethical principles. Future directions for research are identified, focusing on the development of adaptive ethical guidelines, methodologies for embedding ethical principles into AI systems, and the investigation of AI's long-term impact on market dynamics and consumer behaviour. This research contributes valuable insights into the ethical integration of AI in finance, offering recommendations for ensuring that AI technologies are utilized in a manner that is both ethically sound and conducive to the advancement of the financial services industry. Keywords: Artificial Intelligence, Financial Decision-Making, Ethical Implications, Algorithmic Bias, Data Privacy, Regulatory Standards, Multidisciplinary Approach.

Artificial intelligence (AI) applications are widely employed nowadays in almost every industry impacting individuals and society. As many important decisions are now being automated by various AI applications, fairness is fast becoming a vital concern in AI. Moreover, the organizational applications of AI-enabled decision systems have exacerbated this problem by amplifying the pre-existing societal bias and creating new types of biases. Interestingly, the related literature and industry press suggest that AI systems are often biased towards gender. Specifically, AI hiring tools are often biased towards women. Therefore, it is an increasing concern to reconsider the organizational managerial practices for AI-enabled decision systems to bring fairness in decision making. Additionally, organizations should develop fair, ethical internal structures and corporate strategies and governance to manage the gender imbalance in AI recruitment process. Thus, by systematically reviewing and synthesizing the literature, this paper presents a comprehensive overview of the managerial practices taken in relation to gender bias in AI. Our findings indicate that managerial practices include: better fairness governance practices, continuous training on fairness and ethics for all stakeholders, collaborative organizational learning on fairness & demographic characteristics, interdisciplinary approach & understanding of AI ethical principles, Workplace diversity in managerial roles, designing strategies for incorporating algorithmic transparency and accountability & ensuring human in the loop. In this paper, we aim to contribute to the emerging IS literature on AI by presenting a consolidated picture and understanding of this phenomenon. Based on our findings, we indicate direction for future research in IS for the better development and use of AI systems.

Artificial intelligence (AI) technologies have revolutionized numerous sectors, enhancing efficiency, innovation, and convenience. However, AI's rise has highlighted a critical concern: bias within AI algorithms. This study uses a systematic literature review and analysis of real-world case studies to explore the forms, underlying causes, and methods for detecting and mitigating bias in AI. We identify key sources of bias, such as skewed training data and societal influences, and analyze their impact on marginalized communities. Our findings reveal that algorithmic transparency and fairnessaware learning are among the most effective strategies for reducing bias. Additionally, we address the challenges of regulatory frameworks and ethical considerations, advocating for robust accountability mechanisms and ethical development practices. By highlighting future research directions and encouraging collective efforts toward fairness and equity, this study underscores the importance of addressing bias in AI algorithms and upholding ethical standards in AI technologies.

PurposeThe purpose of this study is to critically investigate the effects of artificial intelligence (AI)-driven learning environments on learner autonomy, knowledge co-construction and digital equity in Thailand’s online education context. It aims to analyze how algorithmic learning tools and AI-based systems shape learners' educational experiences, particularly focusing on whether these technologies enhance or constrain learner autonomy and collaborative interactions. Additionally, it evaluates ethical and social implications, particularly the digital divide, algorithmic biases and the transparency of AI-mediated educational processes. The study seeks to provide actionable insights and recommendations for policymakers, educators and developers to optimize AI integration for inclusive lifelong learning.Design/methodology/approachThis research adopts a qualitative case study methodology involving in-depth, semi-structured interviews, participant observations and detailed document analysis from 30 diverse participants, including urban and rural learners, educators and instructional designers. Employing actor-network theory (ANT) and posthumanist theoretical frameworks, the study critically examines interactions between human and non-human actors within AI-driven educational environments. This approach allows for comprehensive exploration of participants' lived experiences, perceptions and interactions with AI technologies. Triangulation of multiple data sources ensures depth and reliability, providing nuanced insights into how AI systems influence learner autonomy, collaborative learning processes and educational equity.FindingsThe study identifies AI-driven learning platforms as having dual impacts on learner autonomy, simultaneously enabling greater self-directed learning through personalized guidance and real-time feedback while imposing algorithmic constraints that restrict intellectual exploration. AI-facilitated knowledge co-construction effectively organizes and structures peer interactions yet often results in depersonalized, transactional communication lacking deep human engagement. Furthermore, significant digital inequities are evident, with rural learners disproportionately disadvantaged due to limited technological infrastructure and algorithmic biases embedded within AI-driven platforms. These inequities highlight a critical need for inclusive, transparent and ethical design practices to ensure equitable access and meaningful learner agency.Research limitations/implicationsThe qualitative, context-specific nature of the case study limits the generalizability of findings beyond Thailand’s online educational environment. The rapidly evolving nature of AI technologies suggests that findings reflect a specific temporal context, necessitating continuous research updates. Participant selection bias may have influenced findings, with participants potentially holding strong pre-existing opinions on AI-driven education. Future studies should consider longitudinal analyses to understand long-term impacts, comparative cross-cultural research to validate findings and deeper exploration of algorithmic biases, fairness and ethical considerations in AI-driven educational tools, particularly how they affect diverse socio-economic groups.Practical implicationsPractically, this research underscores the necessity of designing AI-driven learning platforms that genuinely empower learner autonomy and critical thinking rather than restrict or overly dictate educational pathways. Educational practitioners and technology developers should implement transparent AI decision-making processes, enabling learners to understand and critically evaluate algorithmic recommendations. Policies must prioritize digital infrastructure expansion and digital literacy programs to reduce urban-rural digital disparities. Furthermore, pedagogical practices should integrate human-centric approaches to complement AI tools, ensuring that AI technologies serve to enhance, rather than replace, essential human interactions within collaborative learning environments.Social implicationsThe study emphasizes that AI-driven education holds significant potential for democratizing lifelong learning by offering personalized and adaptive learning experiences. However, it also warns of the risks associated with reinforcing existing socio-economic and digital inequalities, particularly between urban and rural learners. Addressing algorithmic biases and digital divides through targeted policy interventions, infrastructural investments and inclusive educational practices is critical for fostering equity and inclusivity in education. By ensuring that AI platforms are accessible and beneficial for all learner demographics, societies can leverage technology as a tool for broad-based educational empowerment and social development rather than a mechanism perpetuating exclusion.Originality/valueThis research contributes original insights into the nuanced impacts of AI-driven learning on learner autonomy, collaborative interactions and digital equity within the Thai educational context – areas relatively underexplored in existing literature. By uniquely integrating actor-network theory and posthumanist perspectives, it offers a sophisticated understanding of the interplay between humans and technologies in educational settings. The empirical findings deliver crucial evidence-based recommendations for educators, policymakers and developers aiming to ethically and inclusively leverage AI in education. Ultimately, the study enriches ongoing discussions on balancing technological advancement with human-centric, equitable and socially responsible educational practices.