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Feature selection is the most important element of feature engineering to mitigate potential bias in an AI system. This process involves choosing the most relevant and representative features from the data set, which directly affects the model's performance and fairness. By carefully selecting features, data scientists can reduce the influence of biased or irrelevant attributes, ensuring that the AI system is more accurate and equitable. Proper feature selection helps in eliminating biases that might stem from socio-demographic factors or other sensitive variables, leading to a more balanced and fair AI model. Reference: AIGP Body of Knowledge on Fairness in AI and Feature Engineering.

The most challenging aspect of managing a data access request in this scenario is dealing with unstructured data that cannot be easily disentangled from other data, including information about other individuals. Unstructured data, such as free-text inputs or social media posts, often lacks a clear structure and may be intermingled with data from multiple individuals, making it difficult to isolate the specific data related to the requester. This complexity poses significant challenges in complying with data access requests under privacy laws. Reference: AIGP Body of Knowledge on Data Subject Rights and Data Management.

Post-deployment AI maintenance typically includes ensuring that all model components are subject to a control framework, dedicating experts to continually monitor the model output, and evaluating the need for audits under certain standards. However, defining thresholds to conduct new impact assessments is usually part of the initial deployment and ongoing governance processes rather than a maintenance activity. Maintenance focuses more on the operational aspects of the AI system rather than setting new thresholds for impact assessments.

The EU AI Act categorizes certain applications of AI as high-risk due to their potential impact on fundamental rights and safety. High-risk applications include those used in critical areas such as employment, education, and essential public services. A government-run social scoring tool, which assesses individuals based on their social behavior or perceived trustworthiness, falls under this category because of its profound implications for privacy, fairness, and individual rights. This contrasts with other AI applications like resume scanning tools or customer service chatbots, which are generally not classified as high-risk under the EU AI Act.

Establishing a global AI governance infrastructure involves several key elements, including providing training to foster a culture that promotes ethical behavior, creating policies and procedures to manage third-party risk, and understanding differences in norms across countries. While publicly disclosing ethical principles can enhance transparency and trust, it is not a core element necessary for the establishment of a governance infrastructure. The focus is more on internal processes and structures rather than public disclosure. Reference: AIGP Body of Knowledge on AI Governance and Infrastructure.