The text provided does not contain any explicit references to AI-related terms or concepts such as Artificial Intelligence, Algorithm, Machine Learning, or any other related terminology. It primarily lists various public bills and resolutions without addressing the social impact of AI, data governance, system integrity, or robustness in relation to AI technologies. Therefore, it is deemed not relevant to the defined categories of Social Impact, Data Governance, System Integrity, and Robustness.

Similarly, the text does not reference any specific sectors that involve the use or regulation of AI. There are no mentions of how these bills might impact areas such as Politics and Elections, Government Agencies, Healthcare, or any of the other nine predefined sectors. Hence, it is assessed as not relevant to any of the sectors listed.

The text discusses regulations related to automated vessels, specifically focusing on the acceptance and reliability of automated systems intended to replace crew members or reduce crew requirements. This clearly relates to the category of System Integrity, as it outlines the standards and oversight necessary for ensuring that automated systems operate safely and dependably. The text partially aligns with Robustness, discussing requirements for the ongoing reliability of automated vessels, although it primarily emphasizes system acceptance rather than performance benchmarks. There is little direct relevance to Data Governance or Social Impact, as the text does not address data management concerns or the societal implications of these automated systems in detail.

The text relates closely to Government Agencies and Public Services, specifically the Coast Guard's regulations regarding automated vessels. The mention of crew safety and the operational standards expected by the Coast Guard falls into this sector. There isn't a significant connection to the other sectors, as the details are specific to maritime regulations and do not address areas such as healthcare, judicial systems, or political applications.

The text discusses several aspects of automated monitoring systems used in trainsets, which encompasses monitoring components like electric brake status, fire detection systems, and temperature sensors. The relevance of specific categories can be assessed based on their connection to the described monitoring systems and their implications. Social Impact considers the safety and operational implications of automated monitoring, particularly in the context of human interaction and risk; thus, it's very relevant. Data Governance is relevant due to the monitoring capabilities that gather data from various systems; the text emphasizes the need for data accuracy and integrity while also hinting at regulatory oversight. System Integrity is extremely relevant since the text details the self-test features and alerts for failures in the automated monitoring systems, directly connected to the need for reliability and transparency in such systems. Robustness is less relevant, as the discussion does not center on performance benchmarks or certification of AI but rather on operational features of monitoring systems.

The text outlines regulations governing automated monitoring systems within trainsets, touching upon safety protocols and operational readiness that relate to public transportation. The Government Agencies and Public Services sector is highly applicable as the context involves regulations aimed at ensuring safe rail transportation, which is often overseen by government authorities. While elements could somewhat align with other sectors like Judicial System (due to compliance measures), the central theme of regulatory oversight in public services is clearer. The text does not particularly address applications in Politics and Elections, Healthcare, Private Enterprises, Academic Institutions, International Cooperation, or Nonprofits, hence these receive lower relevance.

The text primarily discusses automated systems in terms of telecommunications frequencies assigned for the Automated Maritime Telecommunications System (AMTS). It emphasizes regulation and assignment of frequencies but does not delve into any social implications of AI or automated systems, data management practices, security measures for AI systems, or performance benchmarks for AI. As such, the relevance for Social Impact is negligible, and while it does involve automated systems, it lacks specific language regarding data governance, system integrity or robustness requirements. Hence, scores will reflect this lack of relevance, particularly for categories associated with societal impact, data governance, integrity, or performance benchmarks.

This text touches on automated communication systems, which could relate to Government Agencies and Public Services in the context of communications utilized in public service delivery. However, this relationship is very weak as it does not specifically address how AI or automated systems are being regulated or their broader implications in public service contexts. Other sectors like Politics and Elections, Judicial System, Healthcare, Private Enterprises, Academic Institutions, and others do not find relevance in this context either. Therefore, the scores reflect minimal relevance based on the broader sectors impacted by the text's context.

The text primarily discusses automated vehicle-based inspection systems intended for rail track assessment but does not address broader social impacts associated with AI applications, such as ethical considerations or consumer protections. The relevance to Data Governance is marginal, as it mentions accurate measures but does not delve into data management practices. System Integrity is somewhat relevant due to the discussion about accuracy and compliance standards for the automated systems, while Robustness is not explicitly covered in relation to performance benchmarks or auditing methods. Overall, the focus is on the operational aspects of these automated systems rather than their societal or regulatory implications.

The automated vehicle-based inspection systems are specifically related to the regulation of AI in the context of public transportation infrastructure. Given the emphasis on automating inspections for track safety, there is moderate relevance to Government Agencies and Public Services, as these systems affect governmental oversight and operations of rail services. There is no specific mention of AI use in political regulations, legal systems, healthcare, or labor markets, which diminishes relevance in those sectors. The discussions on automated inspections in this context do not strongly tie into the other sectors either. Therefore, the focus remains primarily within the public services realm.

The text predominantly focuses on the recording and management of manual, electronic, and automated records related to employees in the railroad industry. It does not address the societal implications of AI systems, nor does it reflect upon issues like fairness, bias, or psychological impacts directly associated with AI technologies. There is a reference to 'automated records,' but that does not extend to the broader impacts of AI systems on society, data governance, or system integrity in a way that would necessitate deep legislative consideration. The primary focus appears to be on record-keeping processes rather than direct AI implications, thereby not fitting well into any category.

While the text pertains to the operational processes within a federal transportation context, the relevance of AI applications in these sectors is minimal. It describes procedures for maintaining and accessing various records rather than discussing any AI system's use or regulation therein. The closest relevance could be seen in the electronic and automated records aspect, which mentions data safety and access, but it doesn't directly address AI's applications or implications in these systems. Therefore, scores reflect a lack of strong relevance to the identified sectors.

The text primarily addresses regulations related to the accessibility and operation of Automated Guideway Transit (AGT) vehicles and systems. While there is mention of 'automated' systems, the text does not engage with how AI technology specifically impacts social considerations, data management, system security, or benchmarking for performance, as outlined in the categories. The focus is instead on physical infrastructure and passenger safety pertaining to transit systems, reflecting almost no connection to AI's broader implications. Consequently, the categories of Social Impact, Data Governance, System Integrity, and Robustness are not directly relevant to the discussed framework of accessibility and transit operation.

The text relates to public transport regulations with a focus on accessibility for individuals using mobility aids. There is a brief mention of automated systems, but the overall emphasis is on physical infrastructure aimed at ensuring mobility and safety rather than on deeper issues of AI's role in transport, governance, or society. As such, sectors like Government Agencies and Public Services reflect some relevance, as the regulatory framework is set forth by governmental bodies, but the legislation does not specifically address AI applications within those sectors. Other sectors such as Healthcare, Politics and Elections, Private Enterprises, etc., have minimal or no relevance based on the text content.

The text focuses on the licensing and operational regulations of the Automated Maritime Telecommunications System (AMTS), which revolves around coast stations and maritime communications rather than the deployment, governance, or impacts of AI technologies. The terms related to AI are absent, and there is no mention of algorithms or any form of AI or machine learning. The references to automation pertain to the setup and operation of maritime services rather than autonomous or algorithmic decision-making as it pertains to AI. Therefore, the connection to the four categories of AI-related legislation is minimal.

The text does not pertain to any of the defined sectors as it strictly deals with maritime telecommunications regulations. While it may relate to general communications, it lacks any specific mention or implications regarding political processes, government agency operations, the judicial system, healthcare, labor impacts, academia, international standards, NGOs, or hybrid sectors. Hence, the relevancy to sectors is nonexistent.

The text primarily discusses the requirements for electronic and automated recordkeeping systems used by railroads, detailing the security, accuracy, and integrity of these systems. While these systems may employ some automated processes (hence, the term 'automated recordkeeping system'), there isn't a direct engagement with broader AI technologies or their implications for society. Therefore, the relevance of AI is more focused on operational aspects rather than societal impacts, data governance, system integrity, or robustness of AI systems. As such, the scores reflect only a moderate engagement with the categories related to AI's societal consequences or technical standards, predominantly leaning towards data governance and system security concerns.

The text pertains to the electronic and automated systems adopted by railroads, which indicates relevance to the Government Agencies and Public Services sector, considering it involves regulatory aspects of rail safety and recordkeeping. However, there isn't any direct mention or application of AI in specific areas such as politics, health care, or judicial processes, leading to lower scores for these sectors. Academic and research contexts are also not relevant, as these systems are more applied than theoretical. Therefore, the scores reflect that the text primarily connects to government regulatory actions and somewhat to nonprofit operations but lacks relevance in more specialized or distinct sectors.

The text focuses on automated inspection technology used in railroad settings, which has implications for both social accountability and safety measures related to AI in infrastructure inspection. While not addressing biases or discrimination directly, there are elements involving performance and measurement that suggest a degree of oversight and performance standards for the automated systems, which could be indirectly linked to societal impacts - hence scored moderately relevant. The data collection aspects outlined in automated inspections pertain to data governance, especially concerning the integrity of data and record keeping, thus receiving a higher relevance score. The system's accuracy and procedure requirements resonate with system integrity, and while the mention of performance benchmarks suggests a consideration for robustness, the score reflects a more general connection to AI performance rather than a direct emphasis on setting new performance benchmarks. Therefore, Social Impact receives a score of 3, Data Governance a 4, System Integrity a 4, and Robustness a 3.

The text primarily discusses the application of automated inspection technology within railroad infrastructure, which relates to both government agencies and public services due to the implications of safety and efficiency in public transportation. It does not specifically mention healthcare, politics, or other sectors. The focus on automated inspection indicates a strong connection to Government Agencies and Public Services, thus receiving a high score. Although other sectors could arguably relate, such as Private Enterprises, Labor, and Employment, the emphasis on safety in public service contexts stands out more prominently. Therefore, Government Agencies and Public Services receives a score of 4.

The text primarily focuses on the requirements for automated recordkeeping systems in smaller railroads. It discusses the use of electronic signatures, system security, data retrieval, and record integrity. While it does mention automation in the context of recordkeeping, there is no direct discussion of AI technologies or their implications. The aspects of data security and integrity relate more to governance than to AI's social impact, system integrity concerns, or robustness in terms of AI performance benchmarks. Consequently, it is relevant to the Data Governance category primarily due to the emphasis on record accuracy and security. However, the relevance to the other categories is minimal as they require direct references to AI, which are absent.

The text is related to the operations of railroads and the requirements placed on them concerning electronic recordkeeping systems. The legislation discussed does not explicitly address the use of AI within the railroad sector, nor does it delve into direct applications of AI in any governmental context. It focuses on record management rather than AI applications in government, healthcare, private enterprises, or other sectors. Thus, it has a minimal connection to most sectors, with its closest alignment being Government Agencies and Public Services due to the regulatory framework it entails. Overall, it provides procedural requirements likely aimed at compliance and safety within the railroad sector, which is an indirect association with the sectors mentioned.

The text primarily references a section related to the Federal Communications Commission (FCC) regulations but does not explicitly delve into topics regarding the social impact of AI systems, data governance frameworks concerning AI, the integrity and transparency of AI systems, or performance benchmarks for AI (robustness). There is a mention of 'automated programming systems,' which could be tangentially interpreted as relating to AI but lacks detail relevant to the categories. The overall emphasis is on FCC procedural rules rather than legislative action on AI issues. Hence, the relevance is minimal across all categories.

The text does not address specific sectors directly related to AI applications. While there is an implication of automated systems which might suggest some technological context, there is no direct reference to how these systems interact with politics, government services, healthcare, or any other mentioned sectors. The references are procedural and regulatory in nature, indicating that they do not specifically cover domains where AI impact or implementation is critical. Therefore, all sectors score a 1 due to this lack of direct connection.

The text discusses regulations specifically for the Automated Maritime Telecommunications System (AMTS) and does not explicitly address AI technologies nor their implications on social structures or individual rights. While it touches on automated systems, it focuses on telecommunications regulation rather than the broader societal implications of AI. Consequently, the Social Impact category is rated as slightly relevant due to potential indirect implications of automation on social systems. Data Governance is rated slightly relevant as it pertains to the management of data regarding frequency interference and licensing, but it doesn't specifically address issues of data management in AI contexts. The legislation also addresses system control in automated maritime telecommunications which gives relevance to System Integrity, but again, the text does not delve into AI's role in this. Similarly, Robustness is not directly mentioned, leading us to rate it as slightly relevant due to having unaddressed benchmarks or compliance requirements for automated systems. Overall, none of the categories score higher than 2 due to the absence of direct references to AI and its implications.

The text appears primarily concerned with the use of automated systems in the maritime telecommunications sector, governing operational standards without addressing the AI directly. While the mention of 'automated systems' offers some relevance to Private Enterprises and Public Services, it does not deeply elaborate on their specific usage in legislative terms. This context also has mild connections to Government Agencies based on the regulatory nature of telecommunications services. Additionally, it is not pertinent to Politics and Elections, the Judicial System, Healthcare, Academic institutions, International Cooperation, Nonprofits, or any Hybrid sectors. Therefore, the scores reflect minimal relevance across sectors since the text does not squarely engage with them.

The text focuses primarily on the federal financial participation related to automated data processing systems within the Department of Health and Human Services. It contains sections detailing compliance requirements and regulations for automated systems but does not specifically engage with broader social implications, data governance concerns, system integrity mandates, or performance benchmarking. Given that the references to automation systems revolve around compliance and cost allocation rather than their societal impact or robustness, the relevance of the categories is limited. The text lacks explicit provisions addressing social impact, data governance, or system integrity, and while it pertains to automated systems, it does not frame these within a context of accountability or performance improvement that would fit the robustness category.

The text is relevant to the Government Agencies and Public Services sector as it discusses automated systems used within public assistance programs managed by state agencies and details the requirements for federal financial participation. However, it does not mention specific applications of AI technologies nor does it address how these automated systems interact with sectors like healthcare or the judicial system. Since the focus is on compliance with financial participation and public assistance programs, the relevance to the broader governmental context is limited but noteworthy.

The text mainly revolves around the management and allocation of funds under Title V, specifically regarding federal functions and legality of fund reductions. It doesn't delve into the social implications, ethical aspects, data governance, system integrity, or robustness of AI systems. The references to 'automated data processing' are broad and don't specifically tackle any direct issues related to AI, thus making the overall relevance to AI categories minimal. Given this evaluation, all category scores are on the lower end of the scale.

The content focuses on financial management and the authority of the Secretary regarding funding policies rather than the use of AI in any government, healthcare, or commercial context. There are no references or implications regarding sectors such as politics and elections or healthcare sector applications. Therefore, all sector relevance scores are very low.

The text primarily addresses the stipulations concerning the publication and management of tariffs within a maritime context. It does not specifically mention or imply any elements of artificial intelligence (AI), including aspects related to social impact, data governance, system integrity, or robustness. Therefore, the relevance of the categories to the text is minimal, resulting in low scores across the board.

Similar to the category evaluation, the text does not pertain to any specific sectors relating to AI, such as politics, public services, healthcare, private enterprises, or any other sector. The focus remains solely on maritime commerce and tariff regulations. As a result, there is no relevance to any defined sector regarding AI applications, leading to the lowest scores as well.

The text focuses on transshipment and publication responsibilities for common carriers in relation to automated tariff systems. There are no explicit mentions of AI-related concepts or terminology, nor is there any indication that these responsibilities involve the use of AI systems or technologies. Hence, regulations regarding social impact, data governance, system integrity, or robustness do not apply as there are no relevant AI components within the text.

The text discusses regulations related to common carriers and the publication of tariffs, which does not directly correspond to any of the predefined sectors such as politics, government services, healthcare, or others. The absence of AI applications or implications in these contexts indicates a lack of relevance to any of the defined sectors.

The text does not mention or pertain to any aspects of AI, algorithms, machine learning, or any other AI-related terms or concepts. It focuses solely on federal maritime commission regulations regarding tariffs, rates, and practices for common carriers, which do not involve artificial intelligence or related technologies. Therefore, the categories addressing AI's social impact, data governance, system integrity, and robustness are not relevant.

The text is exclusively focused on the regulations governing non-vessel-operating common carriers and the Federal Maritime Commission's tariff requirements. It does not address any sector-specific uses or regulations of AI technology in politics, government services, healthcare, or any other sector. Thus, all categories regarding specific sectors such as politics and elections, government agencies, the judicial system, healthcare, private enterprises, academia, international cooperation, nonprofits, or emerging sectors receive no relevance concerning AI.

The text primarily deals with regulations around non-vessel-operating common carriers (NVOCCs) and their tariff publications, without any explicit mention of AI concepts. While the term 'automated systems' is noted in the context of access to data, it does not substantiate a strong connection to the social impact of AI, governance of data, assurance of system integrity, or benchmarks for robustness in AI systems. Thus, all evaluated categories will score low due to a lack of relevant AI-related content.

The text pertains to the maritime industry and the regulations governing NVOCCs, which are part of commerce and transportation rather than specific sectors directly relating to AI. There are no references to political processes, government services, healthcare, or any of the listed sectors that would involve AI technology or its governance in any context. Thus, all sector scores will also be low.

The text primarily discusses the integrity of tariffs and related operational procedures in accessing and maintaining tariff data. However, there is no explicit mention of AI or related technologies. Since AI could play a role in automating tariff data management or improving access through algorithms, there could be a slight relevance, but the direct content of the text does not support strong associations with the categories. The focus is very much on existing manual systems and protocols, rather than innovations associated with AI systems. Therefore, the scores reflect that lack of direct relevance while acknowledging a tangential connection to technology itself.

The text pertains to regulatory procedures that govern carriers and their tariff publications, which suggests some relevance to the Government Agencies and Public Services sector as it may involve oversight and access by federal agencies like the Commission. However, it lacks an explicit focus on AI applications in those contexts, limiting its relevance to other sectors like Healthcare or Private Enterprises. The connection to Government Agencies and Public Services is based more on regulation rather than using AI, hence not scoring higher even though the topic is relevant in terms of governance structure.