The text is primarily focused on rules and definitions related to the functioning of the Small Business Administration (SBA) and its regulations concerning investment funds and securities. It does not reference AI technologies or concepts directly or indirectly. Therefore, all categories related to the social impact of AI, data governance, system integrity, and robustness receive low relevance scores. While legislation in these categories may connect to businesses and economic activity, the absence of any AI-related terms makes them irrelevant to this text.

The text lacks any direct references to sectors like politics, government, judicial systems, healthcare, or any sector that deals specifically with AI applications. It instead focuses on the definitions and operations relevant to small business interests and the SBA's authority. Hence, all sector scores are low, as there are no indications that AI influences any of them within this context.

The text primarily discusses regulations related to the prohibition of funding unlawfull internet gambling rather than any AI-specific content. There is a mention of an 'automated clearing house system,' which relates to a financial transaction process but does not connect with AI directly in terms of its implications or applications. Consequently, it does not fit squarely within any of the relevant legislative categories, which focus on direct implications of AI technologies.

The text mainly pertains to regulatory enforcement and definitions concerning internet gambling payment systems without explicit references to the use or regulation of AI within any of the sectors outlined. While patterns in automated transactions could involve algorithms, the text does not emphasize this in a context relevant to any specific sector as defined.

The text primarily concerns civil penalties and procedures related to violations within the jurisdiction of the Nuclear Regulatory Commission (NRC). It does not explicitly address any topics related to AI, automation, or the impact of AI on society, which would be required for inclusion in the Social Impact category. There are also no mentions of data management or governance, meaning the Data Governance category is not applicable. The System Integrity category is also not relevant, as the text does not focus on security, transparency, or control of AI systems. Lastly, it lacks considerations for performance benchmarks or compliance assessments that are typical of the Robustness category. The text is mainly administrative and legal in nature, hence none of the categories hold relevance to the text.

Similarly, the text does not mention AI's use in various sectors. It does not reference AI's application in politics, government services, healthcare, or other defined sectors such as the judicial system or private enterprises. It is focused solely on the administrative procedures and civil penalties enforced by the NRC, thus receiving a score of 1 across all sectors. Overall, the absence of AI-related content or sector-specific references leads to very low relevance in this context.

The text outlines responsibilities within the USDA that are heavily focused on human resources management and environmental compliance, but it does not explicitly mention any AI or related technologies. While there is a reference to 'automated human resources data and systems,' this wording is quite general and does not delve into the implications or governance of AI within human resources, thus diminishing its relevance to the AI categories.

The text relates primarily to administrative operations of human resources management within the USDA and offers insights into procurement and environmental law compliance. It does not address the use of AI in the context of any of the defined sectors, showing a lack of direct relevance to politics and elections, government services, healthcare, or any other discussed sectors. The mention of automated data systems is not sufficiently detailed to warrant significant relevance.

This text relates to agricultural regulations concerning the import of specific vegetables, specifically Irish potatoes and onions. It does not discuss matters related to AI, such as its impact on society, data governance, system integrity, or robustness. The focus is mainly on import regulations, plant health, and specific grading requirements which are not relevant to AI-related issues. Therefore, the legislation does not fit into any of the defined AI categories.

The text pertains solely to agricultural import regulations, specifically about potatoes and onions. It does not involve AI applications in sectors such as politics, government services, healthcare, or any other areas mentioned. No sector involving the regulation or use of AI is identified within the text as the focus is completely on raw agricultural products and their importation. Hence, the relevance to AI sectors is non-existent.

The text focuses on general operating procedures for aircraft, highlighting flight altitudes and traffic patterns in various airspaces. It does not mention any aspects of artificial intelligence (AI), algorithms, data governance, system integrity, robustness, or their impacts. As such, it's irrelevant to all categories related to AI. There are no regulations, societal impacts, data management practices, or benchmarks related to AI performance discussed in this text, which confirms its lack of relevance to the categories provided.

The text describes operating procedures for aircraft and does not address any specific sectors outlined, such as politics, healthcare, or employment. There are no mentions of AI application in any of the sectors, nor does it touch on the regulatory framework governing these areas. Therefore, it is not relevant to any of the sectors listed.

The text primarily focuses on the regulations surrounding Electronic Export Information (EEI) filing to the Automated Export System (AES). It does not specifically mention or imply any developments, effects, or consequences of AI technologies or methods. Therefore, the connection to AI's social impact, data governance, system integrity, or robustness is minimal or non-existent. AI terminology is not referenced, and the text is more concerned with export compliance and control procedures. Thus, it is scored as not relevant to all categories.

The text does not address any specific sector associated with AI, nor does it mention its application within areas such as politics, healthcare, or labor. The focus on the EEI and AES filing is strictly regulatory and does not touch on how AI is used or affected in sectors like government, healthcare, or any related fields. Therefore, all sectors receive the lowest score as they have no connection to the content of the document.

The text does not directly mention AI or related concepts. It focuses on voluntary conveyance of property, loan servicing, and rural development policies without any reference to artificial intelligence technologies or their implications. Therefore, it is not associated with the provision of social impacts related to AI, governance of data within AI systems, or issues pertaining to the integrity or robustness of AI systems.

The text pertains to rural development policies and the conveyance of property by borrowers to the government. It does not address any specific sector related to AI, such as politics, healthcare, or public services, nor does it discuss their intersection with AI technologies. Since it entirely deals with property and loan issues, there is no relevance to the sectors defined.

The text discusses the payment of fees related to the management and storage of elemental mercury, as well as procedural regulations concerning the disclosure of information to the public. There is no explicit mention of AI-related topics. Therefore, none of the categories (Social Impact, Data Governance, System Integrity, Robustness) are relevant to the content as it mainly focuses on financial and administrative regulations rather than any of the outlined AI issues or themes.

The text does not address the use of AI in politics, government services, judicial systems, healthcare, private enterprises, academic institutions, international standards, nonprofits, or any emerging sectors. It is strictly administrative and relates to environmental regulations concerning mercury management. As such, all sectors are assigned a relevance score of 1.

The text primarily revolves around regulations regarding bank disclosures related to deposits and their availability. It does not specifically address AI systems or their societal impacts, data governance, system integrity, or robustness. Therefore, it lacks relevance to the AI-related categories defined. The text does not mention AI, algorithms, or any automated decision-making processes, which are key indicators for categorizing under AI-related legislation. As a result, all categories will receive a low score.

The content of the text does not pertain to any of the nine specified sectors, as it deals exclusively with banking regulations and does not involve AI applications in politics, government agencies, healthcare, employment, or any other sector listed. There is no mention of AI in any context that connects it to the sectors defined. Thus, all sector categories will also have a minimum relevance score.

The text focuses primarily on regulations pertaining to export systems and licensing procedures, specifically the Automated Export System (AES) used for filing Electronic Export Information. Although this pertains to electronic processes, it does not inherently involve AI technologies or concepts. Therefore, the relevance to the listed categories is very limited. There are no explicit references to AI technologies, automated decisions, algorithms, or other pertinent keywords related to the identified categories of social impact, data governance, system integrity, or robustness. Consequently, each category scores low in relevance as the text does not address the implications, governance, or technical considerations of AI applications.

Similar to the category assessment, the text does not relate to any specific sector focus areas regarding the use and regulation of AI. The regulations outlined pertain to export licensing and procedures rather than the application of AI technologies within political, governmental, public service, judicial, healthcare, or other sectors. As a result, all sectors score low as there is no substantive focus addressed pertaining to AI.

The text primarily discusses the application process, eligibility requirements, and operational guidelines for a grant program aimed at supporting rural economic development. There is no reference to AI-related technologies or their impacts, nor does it address ethical concerns, data governance, or system integrity in relation to AI systems. Therefore, all categories relevant to AI legislation, including Social Impact, Data Governance, System Integrity, and Robustness, do not apply here.

The text focuses on grant administration and funding mechanisms for rural development projects and does not pertain to sectors that directly involve AI such as Politics and Elections, Government Agencies and Public Services, Judicial System, or any other specified sectors. Therefore, none of the sectors apply.

The Executive Order 14074 focuses largely on reforming policing practices to enhance public trust and address systemic issues within the criminal justice system. While it discusses the need for data collection and accountability in policing, it does not explicitly address the impacts or regulations regarding AI systems used in law enforcement practices. The connection to AI and its effects on society, data management, system integrity, or the robustness of AI deployments in this context is minimal. Thus, its relevance to AI categories such as Social Impact, Data Governance, System Integrity, and Robustness is limited.

This Executive Order addresses practices within law enforcement, which could involve AI indirectly but does not specifically target the application or regulation of AI in policing or the criminal justice system. While it discusses accountability and data collection, which could relate to data governance, there is no direct mention of AI technology or relevant legislation affecting AI systems within law enforcement. Therefore, while it pertains to the judicial system and public safety, its association with specific sectors like Politics and Elections, Government Agencies and Public Services, or the Judicial System is weak.

The text primarily pertains to regulations surrounding financial institutions, including electronic fund transfers and related practices, but does not mention or imply any AI-related content. While automation could be tangentially related to electronic fund transfers, it is not explicitly referred to in this context. Therefore, overall, the legislation lacks any strong relevance to the specific categories related to AI such as Social Impact, Data Governance, System Integrity, or Robustness.

The text is focused on the regulation of financial institutions and electronic transfers, but it does not address AI usage in the sectors defined. As such, there is no explicit mention of AI's influence in politics, government services, healthcare, or any specified sectors. The absence of AI-specific contexts makes it irrelevant to these sectors as well. The text stands clear of opportunities for linking AI in any particular segment.

The text discusses regulations, knowledge areas, and procedures for aircraft dispatcher certification, primarily centered on aviation safety, weather analysis, navigation, and communications. It does not explicitly mention AI-related technologies or concepts such as machine learning, algorithms, or automation. Given the focus on technical and procedural aspects relevant to aircraft dispatching and regulatory compliance, the categories of Social Impact, Data Governance, System Integrity, and Robustness are not relevant as they don't encompass the details portrayed in this text. There is no indication of AI's influence on social structures, data handling practices, system security, or benchmark performance in AI systems within the air transport context detailed here.

While there could be some peripheral relevance, such as potential use of automated systems in air traffic control or dispatching, the document does not directly address any applications of AI technologies in the sectors defined. The curriculum focuses on compliance with existing aviation regulations and operational procedures without mentioning AI, thus making the relevance of the defined sectors minimal. Hence, all sectors are rated as not relevant as there are no substantive connections to political processes, healthcare applications, or technological impacts on labor that pertain to AI systems.

The text provided is focused on regulations regarding the handling and disclosure of records maintained by government agencies, specifically the USDA, as it pertains to the Privacy Act. However, it does not explicitly reference or pertain to any artificial intelligence (AI) technologies, their applications, or implications. While the principles of data governance and system integrity can be tangentially relevant to the management of records, they are not connected to AI-specific legislation or ethics. No components of this text address social impacts, data governance, system integrity, or robustness concerning AI systems, so all categories will score low relevance.

The text does not mention the use of AI in any capacity within the sectors defined. It primarily deals with privacy and record management as per regulations applicable to government agencies, without specific implications for politics, healthcare, or any of the other sectors listed. The mention of administrative systems does not suggest AI applications nor their regulation, resulting in low scores overall.

The text discusses procedures for the Validated End User (VEU) authorization process, which mainly relates to the export of items and items based on their potential end-use, without explicit mention or focus on AI technologies or their implications. Given this context, there's minimal impact on social issues related to AI, data governance, system integrity or robustness since it deals more with export regulations and compliance. Therefore, it holds little relevance to the AI-related categories defined.

The text outlines procedures relevant to export regulations and the identification of validated end-users, with no direct reference to sectors like politics, healthcare, or employment. While it may implicitly touch upon international business standards, the focus is not on a specific sector where AI is prominently integrated. Thus, it scores low across all defined sectors.

The text provides regulations concerning background checks and physical security related to the production of driver's licenses and identification cards but does not mention AI directly or indirectly through key terms associated with AI technologies such as algorithms, automation, or data governance. It focuses more on procedural compliance and security measures rather than any aspect linked to AI applications or impacts. Therefore, the relevance of the categories is minimal.

The text deals primarily with security and compliance measures for the DMV concerning background checks and employee verification processes. While it falls into regulatory frameworks relevant to government services, it does not specifically address the use of AI or its implications in the outlined sectors. The only slight relevance noted is towards the system integrity category due to inherent security measures mentioned, but overall, the passing references do not strongly tie back to the sector outcomes.