The text does not explicitly refer to Artificial Intelligence or related terminology. However, it discusses automated systems and requires a program of risk analysis and oversight. While some portions relate to the integrity and security of the systems involved without directly mentioning AI, they may touch upon topics relevant to System Integrity and Robustness concerning automated functions. Nevertheless, the main focus remains on operational risk and procedural specifications rather than the broader implications related to AI's societal impact, data governance, or robustness in performance metrics. Thus, the relevance to the categories is minimal, leading to low scores overall.

The text is primarily related to the operational and oversight procedures of swap execution facilities, which does not clearly align with any specific sectors mentioned. It discusses administrative and regulatory processes without delving into specific applications of AI in political contexts, public services, healthcare, etc. Although it hints at aspects of Government Agencies and Public Services indirectly, it is not sufficiently focused on AI applications to justify a higher score. The discussions regarding automated systems are administrative rather than sector-specific, leading to low relevance scores.

The text is a legal regulation regarding communication frequencies for aircraft stations under the Federal Communications Commission. It focuses primarily on frequency allocations and usage policies for aeronautical communication. The legislation does not explicitly cover AI-related themes such as social impacts or data governance regarding artificial intelligence systems. Thus, its relevance to AI categories is minimal. There is no discussion of accountability, fairness metrics, bias, data management, system integrity, performance benchmarks, or robust testing related to AI. Overall, it does not engage with AI in any significant way.

The text is focused on communication frequencies and does not directly pertain to AI applications or implications within any specific sector. It primarily relates to communications regulations and does not reference the role of AI in sectors like healthcare, government services, commercial use, or any other defined sector. Therefore, its relevance to these categories is also very low.

In the text, there are mentions of deep-fake AI and the challenges this technology presents, particularly in the context of financial exploitation and consumer protection. This aligns closely with the Social Impact category as it deals with the implications of AI technologies on consumer safety and trust. The references to scams and fraud also suggest a broader concern about the role of technology, including AI, in financial markets. However, the text does not thoroughly explore themes around data governance concerning AI systems, nor does it provide in-depth discussions on system integrity or robustness related to AI technologies. Therefore, while there's a significant connection to Social Impact, the relevance to the other three categories is limited.

The text primarily pertains to consumer protection in the financial sector, with specific discussions surrounding the operations of the Consumer Financial Protection Bureau (CFPB) and its regulatory actions. The mention of AI in this context suggests its relevance to the financial services sector, particularly in terms of addressing risks posed by emerging technologies such as deep-fake AI in consumer transactions. However, the overall focus is more on regulatory actions and consumer protection rather than the specific application or effects of AI in broader contexts. Therefore, the connection to sectors like Politics and Elections, Judicial System, or Healthcare is weak or nonexistent, leading to lower scores in those categories. The most pertinent connection is to Government Agencies and Public Services due to the role of the CFPB.

This legislation focuses on the protection of minors interacting with social media, which includes potential implications from AI technologies. The references to 'algorithmic recommendation' and 'personalized recommendation system' show a recognition of how algorithms can influence minors' experiences on social media. The legislation suggests a concern for negative psychological effects and seeks to limit harmful content and interactions facilitated by AI-driven platforms. However, it does not delve deeply into the mechanics of the AI technologies or their governance, thus it's more focused directly on social impact rather than broad AI governance.

The bill has direct implications for minors using social media platforms, thus fitting primarily into sectors related to children's safety online. It denotes a clear policy direction regarding how minors should interact with these platforms while ensuring their safety from content linked to emotional distress, which relates heavily to the broader implications on public safety. There is also an implicit connection to government oversight through the role of the Attorney General in enforcing these policies. Although not strictly discussing AI, it indirectly addresses its use in social media contexts.

The text primarily discusses the economic impact of diabetes, focusing on health, societal issues, and treatment costs, but it does not explicitly mention artificial intelligence or any of the related terminology associated with AI. As such, the relevance to the specified categories is minimal, with no mention of social impact through AI, data governance related to AI, the integrity of AI systems, or benchmarks for AI performance.

The text is focused primarily on the economic aspects of diabetes rather than specific AI applications or legislation affecting various sectors. While healthcare might typically relate to AI through diagnostics and treatment optimization, this document does not reference AI in this context. Therefore, there is no relevance to the sectors of politics and elections, government agencies, judicial systems, or any others listed related to AI's application.

The text primarily discusses the appropriations for various environmental and cultural initiatives, particularly focusing on funding for programs related to the Great Lakes, water infrastructure, and museums. It does not contain explicit references to Artificial Intelligence or its applications within environmental or cultural contexts, nor does it touch upon issues such as fairness in AI, data governance, system integrity of AI, or robustness. The text is largely concerned with funding recommendations and descriptions of existing programs without delving into the implications of AI systems or technologies. Hence, it is clear that none of the categories apply to the text.

The text provides a broad overview of funding priorities for environmental and cultural initiatives but lacks specifics regarding the regulation, use, or implications of AI in political or governmental contexts, judicial settings, healthcare, employment, education, or NGOs. While it mentions public services and governmental funding frameworks, it does not address the use of AI technologies within these sectors. Therefore, relevance across the sectors is rated as minimal.

The text outlines listing standards and requirements for compensation committees under the Securities Exchange Act. It does not directly mention any AI-related terms or concepts. Given the focus purely on financial regulations related to the governance of companies and their compensation structures, there are no significant references to issues associated with AI technologies such as machine learning, automated decision-making, or related terms. Thus, all categories are rated as not relevant.

The text does not address the use of AI in any sector or provide any insight into how AI may affect politics, public services, the judicial system, healthcare, private enterprises, academic practices, or international relations. It solely discusses compliance and governance requirements for compensation committees, making all sectors not relevant.

The text primarily focuses on the risk management framework applicable to futures commission merchants, particularly concerning the management of financial and operational risks. It outlines the requirements for establishing a risk management program, detailing policies for mitigating, reporting, and overseeing various types of risks related to financial activities. However, it does not explicitly mention or directly relate to the impact of AI technologies or their governance, nor the integrity or robustness of any AI systems. The reference to 'automated financial risk management controls' indicates a potential relevance to automated systems but lacks a direct focus on AI. Therefore, the relevance to the categories of Social Impact, Data Governance, System Integrity, and Robustness is minimal to moderate at best. Hence, scores are assigned accordingly with none reaching the threshold for relevancy to warrant categorization under these categories.

The text does not directly address specific sectors like politics or healthcare. Instead, it is centered on financial regulations pertaining to risk management in futures commission merchants. While aspects of government regulation are touched on, the overall focus on AI regulation related to various sectors is absent. The mention of automated systems indicates some relevance to Private Enterprises, Labor, and Employment, but not enough to assign a higher score. Overall, the document lacks specificity in relation to the sectors outlined.

The text primarily covers administrative procedures related to the Service Contract Labor Standards statute and various exemptions. There is little to no direct mention of AI technologies or their implications in the context of the provided content. It primarily deals with labor standards, exemptions, and procedures in procurement, which do not align closely with the impacts, governance, integrity, or robustness of AI systems. Thus, the relevance of the identified categories relating to AI is minimal.

The text centers on the administrative aspects of labor standards in federal contracting and does not specifically address sectors like politics, healthcare, or employment in a way that would relate to AI applications, nor does it discuss the implications of AI in public services or other sectors outlined. It largely pertains to procurement policy and labor regulations rather than sector-specific AI legislation or regulation. Hence, all sectors receive the lowest relevance score.

The text explicitly addresses the impact of artificial intelligence on the workforce, job performance, productivity, and training and education requirements, which directly relates to potential social ramifications of AI. It also mentions privacy and security, concerns typically associated with social impacts of technology. Given the prohibitory measure against AI systems displacing workers until a comprehensive study is provided, it strongly reflects legislative intent to protect individuals from AI's adverse effects, thus demonstrating a very relevant connection to social impact. Data governance is moderately relevant, as the text hints at privacy and security but does not provide a comprehensive framework or mandates for data management. System integrity is less relevant, as it does not address the operational or security measures of AI systems, yet it loosely ties into the prohibitions being a safeguard against negative impacts. Robustness is the least relevant, as the text does not delve into benchmarks or auditing of AI systems.

The legislation is explicitly focused on the implications of AI within the workforce and by state entities, making it particularly pertinent to the sector of Private Enterprises, Labor, and Employment. It also indirectly touches on Government Agencies and Public Services due to the involvement of state departments and labor, but the primary focus remains on employment and labor impacts. There is no reference to politics, the judicial system, healthcare, academic institutions, international cooperation, NGOs, or emerging sectors within the proposed act, leading to low relevance ratings across those sectors.

The text primarily discusses regulations regarding the Automated Export System (AES) related to the electronic submission of export information. While there is a mention of processes, approvals, and denials involving government agencies and exporters, it does not address AI directly, nor does it elaborate on system integrity, data governance, social impact aspects, or robustness in the context of AI. Therefore, the relevance of AI-related legislation and its broader implications on society or data management appears minimal.

The text seems to focus on export regulations and processes involving compliance with Customs and Census Bureau requirements rather than the application of AI within the sectors defined. Although it references electronic information systems, it does not specifically engage with any of the sectors mentioned. The minimal mention of electronic systems does not substantively connect the text to the roles AI might play in these sectors.

The text discusses the automation of SNAP operations and the development of an ADP/CIS plan by State agencies, ensuring efficient system management through technology. However, while automation can be linked with AI applications, the text does not directly address issues of social impact resulting from AI, nor does it delve into data governance specifics like bias and privacy concerns or the integrity and benchmarking of AI systems. Hence, its relevance is limited, especially concerning robust ethical implications or broader societal effects.

The focus of the text is primarily on the operational and administrative aspects of SNAP through automation systems developed by State agencies. While automation falls under government services, it doesn't explicitly address how AI is utilized or regulated within this sector, leading to a limited connection. It also does not cover key aspects related to public service AI applications nor the legislative implications within these domains in a significant manner.

The text primarily discusses the structure and requirements for deposit data files. It does not contain any explicit references to Artificial Intelligence (AI), algorithms, machine learning, or related terms. Therefore, it appears to be largely focused on data management as it pertains to financial institutions, rather than the legislative impacts or governance related to AI technologies. As the content lacks discussions on social impacts, data governance measures specifically related to AI, system integrity concerns, or benchmarks for AI performance, all categories score very low relevance.

The text does not explicitly address any governmental functions, judicial systems, healthcare, or any sectors that are related to AI applications. It primarily discusses deposit-related file structures under regulatory compliance, which does not correlate with any of the sectors defined in the prompt. As such, all categories associated with sectors are deemed to be not relevant to the text.

The text primarily addresses security protocols and evaluations within the Department of Energy, specifically around counterintelligence (CI) evaluations and polygraph examinations for individuals with access to classified information. This regulation does not contain explicit references to AI technologies or their implications. Therefore, the risk and impact of AI on society or data management, system integrity, or performance benchmarks are not directly pertinent here, leading to low relevance across all categories related to AI legislation.

The text relates to security and personnel protocols regulated by the Department of Energy, with no direct mention or implication of AI applications. While the protected information and CI evaluations might intersect with the broader public sector's use of technology, it does not specifically acknowledge or regulate AI's role in any sector, from government services to intelligence operations. As such, all scores reflect very limited relevance to AI's involvement within any of the designated sectors.

The text primarily discusses the regulations and procedures related to the Systematic Alien Verification for Entitlements (SAVE) Program, focusing on eligibility verification and data management for applicants of benefits such as SNAP. Although it does include automated verification processes, it does not specifically mention AI or related terms such as algorithms or machine learning. Therefore, the relevance to each category is limited. It could touch on social impacts due to potential implications of data handling and fairness, but the direct connection is not strong. Similarly, system integrity is somewhat relevant because the text outlines procedures for data security and safeguarding, but the absence of explicit AI references diminishes relevance. Data governance has a more moderate relevance due to the focus on data accuracy and security in handling recipient information.

The SAVE Program is relevant to the sector of Government Agencies and Public Services, as it pertains to the verification processes within state agencies for public benefits. However, it does not address AI applications in an explicit manner. While there are discussions on data management and verification, it does not emphasize AI’s role within the judicial context or healthcare, nor does it focus on nonprofits or international standards. Thus, most of the sectors do not apply strongly.

The provided text does not refer to any aspects relating to Artificial Intelligence, algorithms, or other relevant terms. It focuses primarily on administrative and procedural requirements for construction projects, detailing necessary documentation and processes for contractors and borrowers. Therefore, it is not relevant to any of the four categories concerning AI.

The text pertains to construction administration and does not discuss the application of AI in any sector, such as government, healthcare, or any other areas. It centers on requirements for architectural services and construction projects, which do not involve AI technology. Thus, every sector is rated as not relevant.

The text primarily describes various classifications and functionalities of medical devices, particularly automated systems used in hematology. Although the term 'automated' is used frequently, it does not explicitly connect to AI concepts like those in the specified keywords such as AI, algorithms, or machine learning. Therefore, its relevance to the categories is limited. Social Impact could be slightly relevant due to its implications for healthcare delivery, but the link is tenuous. Data Governance is not relevant as data collection or management standards concerning AI systems are not mentioned. System Integrity and Robustness are also not relevant since the text does not discuss security, transparency, or benchmarks for AI performance. Thus, overall, the text has minimal potential relevance to any of the categories defined.

The text focuses mainly on the classification of medical devices and does not delve into any specific legislation or regulations pertaining to the use of AI in sectors such as politics, public services, or healthcare related to AI applications within medical devices comprehensively. The healthcare sector, while relevant, lacks explicit discussions of AI, making its relevance limited. Therefore, overall scores are low across all sectors.

The text primarily focuses on the organizational structure and functions of the Rural Business Service (RBS) within the Department of Agriculture. It discusses the management of various loan programs, community development efforts, and engineering practices related to water and waste facilities in rural America, but does not specifically address any issues related to AI technologies, their governance, or their societal impact. As such, while there may be implications from current legislative practice for data management and grant processing in general, the absence of any explicit mention of AI-related terminology leads to a low relevance for all categories.

The text is centered around administrative functions of RBS and its various programs concerning rural development, loans, and community assistance. There is no indication of AI applications or regulations concerning any of the sectors proposed. Therefore, all sectors receive a score of 1 as they are not relevant to the content of this text.

The text provided primarily focuses on the operational standards and performance requirements for electronic data filing with U.S. Customs and Border Protection. While it discusses issues related to data accuracy and the management of electronic submissions, there is no specific mention of AI-related terminology such as Artificial Intelligence, Algorithms, Machine Learning, or other predefined keywords related to the impact of AI on society. Thus, the text is pertinent to regulatory processes around data management but lacks a direct focus on the broader implications of AI. Due to the absence of explicit AI relevance, the scores for all categories are low.

The text outlines the performance requirements for data systems within Customs procedures, which may imply some relevance to data governance regarding compliance and operational integrity. However, it does not address specific sectors involved with AI usage. The reference here largely revolves around maintaining accuracy in documentation and process integrity, but lacks broader relevance to political sectors or emergent uses of AI that would qualify for higher scores. The final evaluation reflects an assessment that is narrowly focused on data system standards rather than AI's application in various sectors.

The text does not explicitly mention any AI technologies or applications. While it discusses data collection and reporting for financial institutions, it does not delve into issues of social impact, data governance, system integrity, or robustness as they relate specifically to AI. Instead, it focuses on financial regulations and compliance without any reference to automated decision-making or AI systems. Therefore, its relevance to the categories is low.

The text primarily addresses regulations surrounding financial data collection rather than specific applications or impacts of AI within these sectors. Since no explicit mention of AI is present, the relevance to the sectors is minimal. It does touch on the processes used by financial institutions, which is somewhat related to data governance but not significantly so.