Search Results - 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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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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Objective: emergence of digital technologies such as Artificial intelligence became a challenge for states across the world. It brought many risks of the violations of human rights, including right to privacy and the dignity of the person. That is why it is highly relevant to research in this area....

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The article examines the issue of algorithmic transparency as a key principle in regulating artificial intelligence in the context of digital transformation of public administration. It substantiates the need to move from formal disclosure of AI system logic to the creation of mechanisms that ensure...

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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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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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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 this paper we meticulously examined a Word Embedding model in Portuguese, endeavoring to identify gender biases through diverse analytical perspectives, employing SC-WEAT and RIPA metrics that is widely used in the English realm. Our inquiry focused on three primary dimensions: (1) the frequency...

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This study examines the integration of artificial intelligence (AI) in tax law and administration, underscoring key ethical, legal, and socioeconomic dimensions. It explores how AI can improve tax compliance and foster innovation, yet simultaneously raising concerns regarding fairness, transparency...

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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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In recent decades, the rapid development of metaheuristic algorithms has outpaced theoretical understanding, with experimental evaluations often overshadowing rigorous analysis. While nature-inspired optimization methods show promise for various applications, their effectiveness is often limited by...

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The rise of algorithm-driven virtual museums presents a philosophical challenge for how cultural meaning is constructed and critiqued in digital curation. Prevailing approaches highlight important but partial aspects: the loss of aura and authenticity in digital reproductions, efforts to maintain se...

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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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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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The deployment of emotion-recognition technologies expands across healthcare education and gaming sectors to improve human–computer interaction. These systems examine facial expressions together with vocal tone and physiological signals, which include pupil size and electroencephalogram (EEG), to de...

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