Investigating AI systems: examining data and algorithmic bias through hermeneutic reverse engineering by Nishanshi Shukla

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Inside the Black Box: Detecting and Mitigating Algorithmic Bias Across Racialized Groups in College Student-Success Prediction by Denisa Gándara, Hadis Anahideh, Matthew P. Ison, Lorenzo Picchiarini

“…Common approaches to mitigating algorithmic bias are generally ineffective at eliminating disparities in prediction outcomes and accuracy between racialized groups.…”
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Empowering marginalized communities through the digital transformation course by Pelin Yüksel-Arslan, Claudia Plant, Fares Kayali

Subjects: “…algorithmic bias…”
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Work in the Age of Artificial Intelligence by Celso Azmabuja

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Harnessing AI for sustainable higher education: ethical considerations, operational efficiency, and future directions by Sunawar Khan, Tehseen Mazhar, Tariq Shahzad, Muhammad Amir Khan, Ateeq Ur Rehman, Mamoon M. Saeed, Habib Hamam

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AI in enhancing cultural sensitivity by Dwi Mariyono, Mufidul Abror

“…However, challenges such as algorithmic bias and unequal access persist, underscoring the need for ethical frameworks and culturally responsive pedagogies. …”
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Artificial intelligence in healthcare: A focus on the best practices by Haddiya Intissar, Ramdani Sara

“…However, alongside this potential, challenges and ethical considerations remain. Data privacy, algorithmic bias, transparency of AI decision-making, and responsible use are crucial areas that require careful attention. …”
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Application and Legal Governance of Computational Technologies in Credit Investigation: An Interdisciplinary Perspective by Ying Zhang, Zhen Xie, Xiuwei Qu

“…By analyzing the computational technologies and algorithms most commonly used in credit data collection and data storage, data transmission and data access, data analysis and processing, data calculation, result output and effect evaluation, this paper summarizes and proposes a unified general process of modern credit investigation, pointing out that in this general process, low data quality, privacy violation, algorithmic bias are the main challenges in the big data era, and countermeasures like data quality control, privacy protection, and algorithm governance need to be to be taken seriously into account in order to further explore the great potential of the credit investigation under the legal framework.…”
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Assessing the Current Limitations of Large Language Models in Advancing Health Care Education by JaeYong Kim, Bathri Narayan Vajravelu

“…This study aims to evaluate the application of state-of-the-art LLMs in health care education, highlighting the following shortcomings as areas requiring significant and urgent improvements: (1) threats to academic integrity, (2) dissemination of misinformation and risks of automation bias, (3) challenges with information completeness and consistency, (4) inequity of access, (5) risks of algorithmic bias, (6) exhibition of moral instability, (7) technological limitations in plugin tools, and (8) lack of regulatory oversight in addressing legal and ethical challenges. …”
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Discussion on Artificial Intelligence Safety and Ethical Issues by Chen Xinyu, Hui Tianfang, Li Yanlin, Yang Haoyuan

“…Regarding ethical considerations, this paper identifies the origins of algorithmic bias and argues for mitigating it through rigorous testing, validation, and regulatory frameworks. …”
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Regulatory challenges in ai-based diagnostics: Legal implications of ai use in medical diagnostics by Naili Yuris Tri, Mangkunegara Iis Setiawan, Purwono, Baballe Muhammad Ahmad

“…The research results indicated that algorithmic bias, data security, and the requirement for stringent rules to guarantee the ethical and safe application of AI are the primary obstacles. …”
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Artificial Intelligence: An Untapped Opportunity for Equity and Access in STEM Education by Shalece Kohnke, Tiffanie Zaugg

“…Ethical concerns, such as algorithmic bias (e.g., unequal representation in training datasets leading to unfair assessments) and data privacy risks (e.g., potential breaches of sensitive student data), require critical attention to ensure AI systems promote equity rather than exacerbate disparities. …”
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Emerging Technologies Driving Zero Trust Maturity Across Industries by Hrishikesh Joshi

“…Furthermore, the integration of AI and machine learning in Zero Trust frameworks raises questions about data privacy, algorithmic bias, and the need for explainable security decisions. …”
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Distinguishing Reality from AI: Approaches for Detecting Synthetic Content by David Ghiurău, Daniela Elena Popescu

“…Ethical concerns, such as privacy violations, algorithmic bias, false positives, and overreliance on automated systems, are also critically discussed. …”
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Harnessing Artificial Intelligence for ESL Assessments: Efficiency, Challenges, and Future Directions by Seyed Reza Abedi, Farnaz Divanpour, Seyed Reza Molaee, Hailay Tesfay Gebremariam

“…Qualitative insights from 20 instructors reveal challenges, including algorithmic bias, cultural insensitivity, and concerns over data privacy. …”
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AI-Driven Sustainable Marketing in Gulf Cooperation Council Retail: Advancing SDGs Through Smart Channels by Hanadi Salhab, Munif Zoubi, Laith T. Khrais, Huda Estaitia, Lana Harb, Almotasem Al Huniti, Amer Morshed

“…While these benefits are real, data privacy and algorithmic bias remain valid concerns, thus underlining the need for ethics and transparency in the practice of AI. …”
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Integrating Model‐Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation by Karthik Raman, Rukmini Kumar, Cynthia J. Musante, Subha Madhavan

“…However, several challenges, including the availability of relevant, labeled, high‐quality datasets, data privacy concerns, model interpretability, and algorithmic bias, must be carefully managed. Standardization of model architectures, data formats, and validation processes is imperative to ensure reliable and reproducible results. …”
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Advanced Machine Learning and Deep Learning Approaches for Estimating the Remaining Life of EV Batteries—A Review by Daniel H. de la Iglesia, Carlos Chinchilla Corbacho, Jorge Zakour Dib, Vidal Alonso-Secades, Alfonso J. López Rivero

“…In addition, we discuss the ethical implications of DL in RUL estimation, addressing concerns about privacy and algorithmic bias. By synthesizing current knowledge, identifying key research directions, and suggesting methodological improvements, this review serves as a central guide for researchers and practitioners in the rapidly evolving field of EV battery management. …”
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Application of large language models in disease diagnosis and treatment by Xintian Yang, Tongxin Li, Qin Su, Yaling Liu, Chenxi Kang, Yong Lyu, Lina Zhao, Yongzhan Nie, Yanglin Pan, Yuanyuan Ji

“…Despite these promising developments, significant challenges persist regarding the use of LLMs in medicine, including concerns regarding algorithmic bias, the potential for hallucinations, and the need for rigorous clinical validation. …”
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An analysis of artificial intelligence automation in digital music streaming platforms for improving consumer subscription responses: a review by Nontokozo Mokoena, Ibidun Christiana Obagbuwa

“…Potential challenges related to privacy, ethics, and algorithmic biases are also discussed, showcasing how AI is revolutionizing the music streaming industry.…”
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