Algorithmic discrimination: examining its types and regulatory measures with emphasis on US legal practices.

Introduction: Algorithmic decision-making systems are widely used in various sectors, including criminal justice, employment, and education. While these systems are celebrated for their potential to enhance efficiency and objectivity, they also pose risks of perpetuating and amplifying societal biases and discrimination. This paper aims to provide an indepth analysis of the types of algorithmic discrimination, exploring both the challenges and potential solutions. Methods: The methodology includes a systematic literature review, analysis of legal documents, and comparative case studies across different geographic regions and sectors. This multifaceted approach allows for a thorough exploration of the complexity of algorithmic bias and its regulation. Results: We identify five primary types of algorithmic bias: bias by algorithmic agents, discrimination based on feature selection, proxy discrimination, disparate impact, and targeted advertising. The analysis of the U.S. legal and regulatory framework reveals a landscape of principled regulations, preventive controls, consequential liability, self-regulation, and heteronomy regulation. A comparative perspective is also provided by examining the status of algorithmic fairness in the EU, Canada, Australia, and Asia. Conclusion: Real-world impacts are demonstrated through case studies focusing on criminal risk assessments and hiring algorithms, illustrating the tangible effects of algorithmic discrimination. The paper concludes with recommendations for interdisciplinary research, proactive policy development, public awareness, and ongoing monitoring to promote fairness and accountability in algorithmic decision-making. As the use of AI and automated systems expands globally, this work highlights the importance of developing comprehensive, adaptive approaches to combat algorithmic discrimination and ensure the socially responsible deployment of these powerful technologies.

Unlabeled data selection for active learning in image classification.

Active Learning has emerged as a viable solution for addressing the challenge of labeling extensive amounts of data in data-intensive applications such as computer vision and neural machine translation. The main objective of Active Learning is to automatically identify a subset of unlabeled data samples for annotation. This identification process is based on an acquisition function that assesses the value of each sample for model training. In the context of computer vision, image classification is a crucial task that typically requires a substantial training dataset. This research paper introduces innovative selection methods within the Active Learning framework, aiming to identify informative images from unlabeled datasets while minimizing the number of required training data. The proposed methods, namely Similari-ty-based Selection, Prediction Probability-based Selection, and Competence-based Active Learning, have been extensively evaluated through experiments conducted on popular datasets like Cifar10 and Cifar100. The experimental results demonstrate that the proposed methods outperform random selection and conventional selection techniques. The superior performance of the novel selection methods underscores their effectiveness in enhancing the Active Learning process for image classification tasks.

Constructing a multi-functional small urban green space network for green space equity in urban built-up areas: A case study of Harbin, China.

Ensuring equitable access to green spaces in urban built-up areas is not only vital for fostering environmental justice but also aligns with the United Nations Sustainable Development Goals (SDGs). However, there is a noticeable gap in the current body of research regarding the role of small urban green spaces, especially their multifunctionality from an ecosystem services perspective. Taking the urban built-up area of Harbin as an example, this study first applied the Analytic Hierarchy Process to classify the supply and demand of green space into three types. Then, the article further analyzes the potential functional positioning of the newly added green spaces, including ecological and social functions, using Minimum Cumulative Resistance and Point of Interest. Finally, multi-criteria decision models are used to explore the priority and functional positioning of green space and construct a multi-functional and highly-efficient small urban green space network. The results indicate a significant imbalance in green space supply and demand, with severe and medium mismatch areas accounting for 30.17 % and 48.50 %, respectively. By assessing the multifunctionality of small green spaces, we propose guidelines that include five types of areas: Concentrated Development (85.85 km2, 16.94 %), Backup Development (70.74 km2, 14.31 %), Maintenance (304.49 km2, 61.51 %), Protection (14.94 km2, 3.02 %), and Optimization (20.89 km2, 4.22 %). Finally, the article proposes a 277.60 km multifunctional small urban green space network. By examining small urban green spaces, this study crafts a pivotal framework for enhancing green space equity in urban built-up environments, providing valuable insights for policymakers and urban planners. The approach has significant implications for developing multifunctional green networks in varied urban contexts and offers a model for wider application, serving as a reference for achieving green space equity in developing countries globally.

Digital intervention in improving the outcomes of mental health among LGBTQ+ youth: a systematic review.

LGBTQ+ youth experience mental health disparities and higher rates of mental disorders due to barriers to accessing care, including insufficient services and the anticipated stigma of revealing their identities. This systematic review incorporated 15 empirical studies on digital interventions' impact on LGBTQ+ youth mental health, examining their potential to address these inequities. This study innovatively categorized existing digital interventions into four streams: Structured Formal (telehealth, online programs), Structured Informal (serious games), Unstructured Formal (mobile applications), and Unstructured Informal (social media). We found that S&F and U&F effectively reduced symptoms. U&F showed potential but required enhancement, while U&I fostered resilience but posed risks. Further integration of emerging technologies like virtual reality may strengthen these interventions. This review identifies the characteristics of effective digital health interventions and evaluates the overall potential of digital technologies in improving LGBTQ+ youth mental health, uniquely contributing insights on digital solutions advancing LGBTQ+ youth mental healthcare.

Association of Cytoplasmic CXCR4 With Loss of Epithelial Marker and Activation of ERK1/2 and AKT Signaling Pathways in Non-Small-Cell Lung Cancer.

OBJECTIVES: Compelling evidence demonstrates that CXC-chemokine receptor 4 (CXCR4) is involved in tumor invasion, angiogenesis, metastasis, and resistance to chemotherapy in addition to being one of the coreceptors for T-tropic human immunodeficiency virus entry into T cells. However, it remains controversial as to how to identify functionally activated CXCR4 in tumor biopsies, which would assist in determining which patients may benefit from potential CXCR4-targeted therapy. MATERIALS AND METHODS: Immunohistochemistry (IHC) staining on archival tissues of patients with non-small-cell lung cancer (NSCLC) was used to detect a panel of biomarkers, including phospho-ERK1/2, phospho-AKT, and E-cadherin, which are relevant to downstream signaling of CXCR4 and epithelial to mesenchymal transition (EMT). We also examined whether subcellular localization of CXCR4 could help define possible activation of CXCR4. RESULTS: A total of 94 primary tumor tissue samples from patients with NSCLC were included. Sixty-six patients had both cytomembranous and nuclear staining of CXCR4, 22 had solely nuclear staining, 5 had solely cytomembranous staining, and 1 had negative staining. Cytoplasmic location of CXCR4 with or without nuclear location was associated with loss of the epithelial marker E-cadherin (P = .0015) and activation of ERK1/2 (P = .0121) and AKT (P = .0024), suggesting EMT in these tumors; whereas tumors with only nuclear location of CXCR4 were more indolent and preserved an epithelial phenotype. CONCLUSIONS: Our study suggests that different subcellular localization of CXCR4 may be associated with different activation states; cytoplasmic CXCR4 seems to correlate with biomarker changes associated with EMT in NSCLC.