Søgeresultater - "Algorithmic bias"

In this paper, we introduce a framework to evaluate the economic implications of algorithmic bias specifically for the emergency response systems that incorporate AI. Unlike existing research, which mostly addresses technical or ethical aspects in isolation, our approach integrates economic theory w...

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This research focuses on pre-university students’ perceptions of algorithmic biases in artificial intelligence. Six types of biases (generational, gender, functional diversity, ethnicity, geographical origin and economic reasons) are examined on the basis of four variables (age, sex, educational lev...

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Active development and implementation of artificial intelligence technologies (AI) in various spheres of human activity have started the processes of qualitative change in public relations. This fact necessitates the development of legal and technical standards to regulate AI technologies. In this r...

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This article analyzes the role of legal interpretation in mitigating algorithmic bias within electronic contracts. In a digital era where algorithms increasingly influence the formation, interpretation, and execution of contracts, algorithmic bias can create significant imbalances between contractin...

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Probability calibration is commonly utilized to enhance the reliability and interpretability of probabilistic classifiers, yet its potential for reducing algorithmic bias remains under-explored. In this study, the role of probability calibration techniques in mitigating bias associated with sensitiv...

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This article examines algorithmic bias as a pressing legal challenge, situating the issue within the broader context of artificial intelligence (AI) governance. We employed comparative legal analysis and reviewed pertinent regulatory documents to examine how the fragmented U.S. approaches and the EU...

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In contemporary society, security has acquired exceptional importance, prompting increasing interest in intelligent video surveillance. The aim of this study was to conduct a systematic analysis of current approaches to AI-powered video analytics, encompassing technological algorithms, legal constra...

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Background: Artificial intelligence (AI) is rapidly transforming vascular care by enhancing diagnostic accuracy, streamlining pre-interventional planning, and improving patient outcomes. Given the inherent complexity of vascular conditions and the emergence of big data, AI has emerged as a promising...

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Laura J Horsfall, Paulina Bondaronek, Julia Ive, Shoba Poduval Institute of Health Informatics, University College London, London, UKCorrespondence: Laura J Horsfall, Email laura.horsfall@ucl.ac.ukAbstract: Clinical algorithms are widely used tools for predicting, diagnosing, and managing diseases....

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Abstract This study investigates the role of symbolic content, including social, cultural, and political imagery, in shaping algorithmic biases within YouTube’s recommendation system, using the 2024 Taiwanese presidential election as a case study. Leveraging classification methodology and a dataset...

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Objective: to identify the hidden ecological costs associated with the elaboration, implementation and development of artificial intelligence technologies, in order to ensure its sustainable and harmonious integration with various economic sectors by identifying optimal moral-ethical and political-l...

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Machine learning significantly enhances clinical decision-making quality, directly impacting patient care with early diagnosis, personalized treatment,  and predictive analytics. Nonetheless, the increasing proliferation of such ML applications in practice raises potential ethical and regulatory obs...

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The contemporary legitimacy crisis in American policing, amplified by high-profile incidents of misconduct and long-standing racial and socioeconomic inequities, has spurred urgent demands for structural reform. Central to these reforms is the adoption of artificial intelligence (AI) and surveillanc...

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AI-driven healthcare systems perpetuate gendered and racialised health inequalities, misdiagnosing marginalised populations due to historical exclusions in medical research and dataset construction. These disparities are further reinforced by androcentric medical epistemologies where white male bodi...

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The rise of automated decision-making in government poses both benefits and challenges. This article identifies and examines legal considerations relevant to governments and businesses in automating existing decision-making processes, focussing on planning permits and building approvals as a case st...

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Social media powered by AI has become a major means for influencing beliefs and behaviors. Its unprecedented analytical, personalization, and scaling capabilities could transform economic, health, and other development outcomes in low- and middle-income countries (LMICs). However, issues associated...

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This paper investigates how people perceive artificial intelligence (AI), using both original survey data and insights from recent academic and policy literature. As AI technologies become increasingly embedded in daily life—from banking and healthcare to education and justice systems—it is importan...

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The exponential growth of scientific literature presents significant challenges for researchers conducting systematic reviews and meta-analyses, particularly in public and occupational health. Artificial intelligence (AI) offers transformative potential to streamline these processes by expediting li...

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Fairness is a fundamental virtue in machine learning systems, alongside with four other critical virtues: Accountability, Transparency, Ethics, and Performance (FATE + Performance). Ensuring fairness has been a central research focus, leading to the development of various mitigation strategies in th...

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