The text provided primarily details changes to transportation laws and does not mention AI technologies or their impact on society, data governance, system integrity, or robustness. Therefore, none of the categories are applicable as the text does not engage with AI principles or frameworks.

The legislation's focus is on transportation and administrative issues related to the Department of Transportation in North Carolina, with no references to the implications of AI in politics, public services, healthcare, or any relevant sector. Therefore, it cannot be considered relevant to any of the defined sectors for AI legislation.

The text primarily discusses provisions related to transportation, economic development, and environmental conservation, with specific legislative sections addressing aspects like mass transportation funding and logistics. The only mention of AI comes in the context of establishing an artificial intelligence research program and consortium, which indicates that while AI is referenced, it is not the primary focus of the legislation. Thus, relevance to categories discussing broader social impact, data governance, system integrity, and robustness are limited. However, the inclusion of AI in the manifest indicates some level of consideration of its implications, particularly in terms of social effects. The Social Impact category is more relevant due to the potential implications of AI research on society, although other categories have minimal direct connections to AI-specific considerations.

In terms of sector relevance, the AI reference indicates a consideration towards integrating AI within public services and economic initiatives, which can be a part of Government Agencies and Public Services. There could also be implications for Academic and Research Institutions, given the establishment of an AI research program. However, other sectors do not find a fitting connection, as the text does not explicitly cover legislation specific to healthcare, private enterprises, or judicial use of AI. Some mention could be made regarding non-profits if they are involved with the AI consortium; however, this isn’t detailed in the text. Thus, the most suitable scoring will focus on the government aspect as it relates to AI.

The text discusses legislation that mandates the integration and deployment of advanced technologies, particularly in border security operations, explicitly mentioning 'artificial intelligence' and 'machine learning' as key components of these technologies. This directly relates to several aspects of social impact, data governance, system integrity, and robustness, as it outlines how these technologies may affect communities, ensure the secure handling of data, maintain system security and transparency, and set benchmarks for performance. Given this explicit reference to AI technologies in a regulatory context, all four categories demonstrate clear relevance to the bill.

The legislation falls within the domain of government operations, specifically the Department of Homeland Security, which seeks to apply innovations including AI to border security processes. It outlines the roles of various agencies in implementing these technologies and assessing their impact, making it particularly relevant to government agencies and public services. It also touches on possible security impacts on border communities, emphasizing its relevance to societal considerations and governance. However, sectors like healthcare, private enterprises, and others are not addressed by this particular bill, highlighting its targeted focus.

The text primarily pertains to consumer protection in relation to reproductive health data. For the **Social Impact** category, it is relevant because the legislation significantly affects individuals' privacy and control over their reproductive health data, an area where AI could potentially play a role in data processing and algorithmic inference. It also addresses biases and the importance of informed consent, which links to broader social concerns about equity in AI systems. In **Data Governance**, this legislation is directly relevant as it regulates the collection, processing, and selling of reproductive health data, ensuring that such data is handled securely and legally. **System Integrity** is moderately relevant due to the emphasis on data handling, privacy policies, and consent management; however, the text does not specifically address system transparency or oversight mechanisms. Regarding **Robustness**, the legislation does not set benchmarks or auditing for AI systems, focusing instead on consent and data protection rather than performance metrics; therefore, relevance is lower. Thus, scores reflect these insights.

The text is primarily focused on legislation concerning privacy and protection of reproductive health data. There is no direct discussion of AI's use in **Politics and Elections**, and while there may be some relevance to **Government Agencies and Public Services** in how health data is managed by such entities, it is not explicitly addressed. The **Judicial System** aspect is not significant since the bill is more about data protection than legal processes. The **Healthcare** sector is highly relevant, as it deals directly with reproductive health data and services. The text pertains to **Private Enterprises, Labor, and Employment** somewhat since businesses are regulated in how they process sensitive health data, while it does not address academic use directly. International cooperation and nonprofit sectors are not mentioned. Therefore, I have assigned scores accordingly, with a strong focus on healthcare and data protection aspects.

The text primarily revolves around the legal framework for electronic estate planning documents and remote witnessing, with no specific focus or language directly related to the core concerns of AI technologies and their implications. While there are references to electronic records and signatures, the legislation does not delve into how AI systems, algorithms, or automated decision-making processes affect these processes. It lacks discussions on societal impacts of AI, data governance concerns such as bias in data used for electronic signatures, or security and integrity issues that can arise from AI technologies. Therefore, overall relevance to AI-related legislation on the specified categories is minimal.

The text does not specifically address how AI is used in sectors such as politics, government, or any other identified categories. It mainly focuses on the legal execution of electronic documents and the witnessing process without mentioning potential AI applications or implications within these contexts. Hence, it is not clearly related to the sectors defined.

The text directly addresses the development of online services, products, or features targeted at children, which necessitates significant considerations related to the social impact of technology, particularly in safeguarding children's data and ensuring they are not exposed to harmful content. Issues like automated decision-making, security implications of algorithms that may be used in these services, and the need for transparency in how children's data is processed are critical, highlighting the relevance to both social impact and system integrity categories. It indirectly applies to data governance through its emphasis on protecting children's personal data as well as the processes involved in collecting and managing this data responsibly.

The sector relevance is centered predominantly around the implications of AI and technology in products targeting children. This is especially pertinent in the context of online services that may utilize algorithms for personalization or decision-making. The bill outlines regulations that apply to covered entities dealing with children's personal data, which relates to all sectors but particularly highlights the Government Agencies' regulatory role and the implications for Private Enterprises involved in the digital space for children. The lack of explicit references to legal systems or healthcare settings in the text suggests lower relevancy for those sectors. There’s some relevance for Academic and Research Institutions around studying the effects of AI in child-focused services, yet it doesn't directly target these sectors.

This text emphasizes the requirements and regulations surrounding electronic health records (EHR), with a significant focus on the use of artificial intelligence (AI) in determining diagnostics and treatment suggestions based on patient medical records. Since AI is mentioned explicitly in the context of healthcare decisions, there is a strong relevance to the Social Impact category, particularly regarding consumer safety and healthcare accuracy. Furthermore, the text contains data governance aspects related to how electronic health records are managed and stored, especially concerning accuracy and access restrictions shaped by AI tools. The System Integrity category is also relevant due to the implications of ensuring human oversight on AI processes in healthcare. Robustness is less relevant because it does not mention benchmarks for performance or compliance measures extensively. Overall, this legislation directly addresses social issues (accuracy of AI in treatment), data management (EHR requirements), and the need for human oversight, which adds weight to the relevance of these categories.

The text primarily pertains to healthcare as it deals explicitly with electronic health records and AI usage in medical contexts. It outlines important legal frameworks that govern how healthcare practitioners interact with AI technology and manage patient information, especially concerning diagnostic procedures and recommendations. While the legislation touches upon AI implications in healthcare, there is minimal relevance to politics, the judicial system, private enterprises, academic institutions, nonprofits, or international standards, as it mainly focuses on specific healthcare practice regulations. As such, the strongest alignment is with the healthcare sector, reflecting a direct influence of AI within medical practice.

The text explicitly discusses various legal frameworks concerning artificial intelligence (AI) technology, particularly focusing on accountability, consumer protection regarding synthetic content, and implications for legal proceedings. This aligns well with the Social Impact category, as it addresses accountability and harm related to the misuse of AI technology. The Data Governance category is relevant due to mentions of consumer warnings and the handling of AI-generated synthetic content which connects to data management and consent. System Integrity is also relevant since it highlights the necessity of judicial assessments of AI evidence, indicating concerns over security and control of AI systems. Robustness is less central as the text does not primarily address benchmarks for AI performance but rather legal definitions and implications. Overall, the relevance of the Social Impact is strong due to provisions on misuse and consumer rights, while Data Governance and System Integrity are moderately relevant as they touch upon data management and legal standards for evidence respectively.

The text pertains primarily to the sectors of Government Agencies and Public Services, as it discusses legislation that directs state regulatory bodies on AI-related consumer rights and judicial practices. It indirectly touches on the Judicial System due to its focus on evidence and verification processes concerning AI. The discussion about consumer warnings and liabilities is particularly relevant to Private Enterprises, Labor, and Employment, as it relates to businesses dealing with AI technology. While there are implications for healthcare and academic institutions, these are much less pronounced, thus scoring lower. The legislation primarily focuses on governmental and consumer implications arising from AI technology, giving it a distinct connection to governmental functions and legal statutes.

The text primarily addresses fair housing protections and the role of the Department of Housing and Urban Development (HUD) in enforcing those protections. While AI is mentioned in relation to housing practices, such as advertisement delivery and automated mortgage underwriting, these instances do not dominate the text. The category of Social Impact is relevant as it pertains to discrimination and consumer protections linked to AI use in housing contexts, thus scoring high relevance. Data Governance receives moderate relevance because the mention of AI in the context of fair housing monitoring implicates data management, but it is not the central focus of the bill. System Integrity and Robustness are less relevant as there are no key mandates regarding the security, transparency, or performance of AI systems articulated within the text.

The text primarily pertains to housing and the regulatory frameworks surrounding fair housing laws. It particularly examines the dynamics of these laws in connection with digital platforms and AI application in housing practices, making it relevant to several sectors. The Healthcare sector is not applicable, nor are sectors relating to Judicial Systems, Nonprofits and NGOs since they are not central to the bill’s focus. The Government Agencies and Public Services sector receives moderate relevance given the mention of the Department of HUD, which is a government agency tasked with public service objectives. The Private Enterprises, Labor, and Employment sector is also moderately relevant, given its implications for housing markets. The Academic and Research Institutions sector is less relevant as there is no focus on educational frameworks. There is no legislative content that squarely fits into Politics and Elections or International Cooperation and Standards, nor does the text reflect any emerging or hybrid aspects that would qualify it for those sectors.

The text discusses the generation of wildfire susceptibility and vulnerability maps, with a requirement for the University of Hawaii to implement a program to produce these maps. While the text includes the term 'Artificial Intelligence' in the report title, it does not mention AI directly in the main body of the text or provide context on how AI might be integrated into this endeavor. Therefore, AI's relevance to the text remains vague, which impacts the assessment of the four categories. Without clear mention of AI's implications on social aspects, data management, system integrity, or performance robustness within the context of wildfire susceptibility mapping, the scores assigned to each category remain low. This mainly reflects the lack of substantive focus on how AI contributes to the intended outcomes of the text.

The text predominantly revolves around the environmental management and public safety sector, specifically dealing with natural disasters such as wildfires. As the text does not detail the application of AI that relates to politics, government actions, the judicial system, healthcare, or other specified sectors, its relevance across the nine sectors is limited. The references to 'wildfire management' and 'public safety' suggest connection to the 'Government Agencies and Public Services' sector, but without explicit AI applications, the scores assigned are minimal across the sectors.

The text discusses the involvement of artificial intelligence in precision agriculture, particularly in Section 4 where the impact of AI on precision agriculture is considered. This suggests a connection to the social, ethical, and operational implications of AI applications in agricultural practices. However, the specifics of these implications are not elaborated on in detail, making it necessary to scrutinize how this context fits within the broader category definitions. While AI's role within precision agriculture could affect social impact through efficiency, data governance through the data it manipulates, and system integrity via the operational frameworks it relies upon, the lack of detailed implications limits the relevance to robustness metrics specifically. Thus, it is of moderate relevance to social impact and data governance, while demonstrating a level of importance for system integrity as well as robustness due to its mention of standards and interconnectivity. Overall, the act promotes the efficient use of technology in a sector heavily reliant on data, suggesting moderate to high relevance across categories.

This bill has direct relevance to the 'Government Agencies and Public Services' sector, as it pertains to government-led initiatives in precision agriculture involving AI technologies. It also has implications for 'Private Enterprises, Labor, and Employment' as it relates to agricultural practices and technologies that can directly impact labor markets and business efficiency. Moreover, it fits into 'Academic and Research Institutions' since the development of standards and research is a critical focus to improve precision agriculture methodologies. However, it does not primarily address legislative concerns of the 'Judicial System', 'Healthcare', or directly relate to 'Nonprofits and NGOs,' making those categories less relevant. The broader application of AI in agricultural contexts indicates a need for multi-sectoral collaboration especially considering environmental and efficiency concerns.

The text extensively discusses how artificial intelligence (AI), particularly through the use of deepfakes and algorithmically generated disinformation, poses risks to electoral integrity and public trust. It specifically mentions 'deepfakes' and acknowledges that California is entering an 'AI election' where AI-generated content could mislead voters. This indicates a strong connection to societal impact due to the potential consequences of AI in elections, thereby falling under the Social Impact category. Additionally, the legislation's focus on deceptive practices in advertisements strongly ties to the impact on consumer trust and accountability of system developers. For Data Governance, the text emphasizes controlling the distribution of misleading content, particularly through elections, but does not touch on data management beyond user-generated content implications. However, it highlights the need for transparency and labeling, suggesting a foundational aspect of governance related to AI use. The System Integrity aspect isn’t as prominent, as the text mainly deals with regulation of media rather than the security or oversight of AI systems themselves. Robustness is also not particularly relevant since there are no mentions of performance standards or compliance checks for AI systems. Overall, the most relevance is given to Social Impact.

The legislation directly addresses the role of AI in political campaigns through the regulation of deceptive media used in elections. It outlines how disinformation, especially via deepfake technology, can undermine electoral processes, hence fitting strongly into the Politics and Elections sector. While there are mentions of implications for government integrity and public trust which indirectly relate to Government Agencies and Public Services, they are not the main focus of the text. There are no discussions relevant to the judicial use of AI, healthcare applications, or the specific impacts on employment or labor markets. The academic sector is not addressed as well, nor are there explicit mentions of international or nonprofit dynamics surrounding AI regulation. Therefore, the most pertinent is Politics and Elections.

The text explicitly addresses the responsibilities of content creators regarding the use of artificial intelligence in generating content that may be misleading or deceptive when presented to consumers. This aspect directly relates to the category of Social Impact, as it involves consumer protection against potential AI-generated misinformation and ensures transparency in the use of AI-generated media. It also touches on Data Governance, as it discusses the handling and disclosure of AI-generated content, which connects with the collection and management of data. System Integrity is seen through the requirements for clear and conspicuous disclosures, which aim to protect consumers from automated decisions that could mislead them. Robustness is somewhat relevant due to the emphasis on standards for clarity in disclosure but is less directly connected since it does not focus on benchmarks for AI performance or auditing. Overall, Social Impact and Data Governance are the most relevant categories here, while System Integrity has some relevance.

The text has direct implications for the regulation of AI within the realm of consumer protection, which affects a variety of sectors. Specifically, it relates to Private Enterprises, Labor, and Employment, as it impacts businesses that generate AI content, mandating transparency in their practices. It affects Government Agencies and Public Services by setting standards that may influence public policy regarding consumer protection and AI use. The provisions regarding AI-generated content could also have implications for Political and Elections when considering how AI-generated materials may affect political campaigns. However, there are no explicit mentions of other sectors such as Healthcare or the Judicial System, which makes those less relevant. Therefore, the most relevant sectors are identified as Private Enterprises, Labor, and Employment, followed by Government Agencies and Public Services.

The legislation addresses significant aspects of AI and its implications for social media use among minors. The relevance to 'Social Impact' stems from its focus on mental health issues linked to social media use and the specific protections for vulnerable groups. In terms of 'Data Governance,' it lays out strict requirements regarding user data collection, privacy, and consent, particularly aimed at minors, which is a focus of this category. 'System Integrity' is relevant as the Act mandates transparency and control over how user data is processed by algorithmic recommendation systems. Lastly, 'Robustness' is also relevant because it deals with the standards and practices around algorithmic use, ensuring platforms uphold user rights effectively and prevent misuse of AI technologies. The interplay between algorithmic recommendations and user privacy forms a significant communication within the text, impacting multiple categories. Therefore, it's rated highly across all categories.

The legislation has wide-reaching implications for various sectors. In terms of 'Politics and Elections,' it does not directly apply as it is not about electoral processes. However, in 'Government Agencies and Public Services,' it applies as it regulates social media platforms, which could be considered public services in the digital realm. Given the focus on protecting minors, the 'Judicial System' is affected due to potential liabilities imposed on platform operators for misuse of data. 'Healthcare' relates to addressing mental health issues stemming from social media use, making it moderately relevant. 'Private Enterprises, Labor, and Employment' may be affected as companies may need to adapt to new recommendations on data use. 'Academic and Research Institutions' could have involvement through studies on AI and social media impact. 'International Cooperation and Standards' could appear as platforms operating multinationally may be influenced by such legislation. 'Hybrid, Emerging, and Unclassified' applies since this law does not strictly conform to traditional sectors. Overall, the text has strong implications for multiple sectors, particularly those relating to user privacy and safety.

The text centers on the environmental impacts of artificial intelligence, which ties directly into societal concerns regarding how AI affects the environment. This means it has implications on various elements relevant to 'Social Impact,' such as influencing energy consumption, pollution, and e-waste. Thus, this category is extremely relevant. The 'Data Governance' category is less about AI lifecycle impacts or environmental data management, making it less relevant, but it does touch on managing and reporting data about these impacts, leading to a score that reflects moderate relevance. The text also emphasizes the need for transparency and accountability in measuring AI's environmental impact, aligning it more closely with 'System Integrity,' but it's not about security or control directly, resulting in a moderate score. 'Robustness' is not particularly addressed in terms of AI performance benchmarks, so it scores lower on relevance as it does not fit within this legislative focus.

The legislation focuses on the environmental implications of AI, making it highly relevant to Environmental policy sectors. While it doesn’t specifically touch on sectors like Politics and Elections or the Judicial System, its relevance may extend to broader implications for Government Agencies and Public Services given collaboration with agencies like the EPA and NIST. It doesn’t address sectors like Healthcare or Private Enterprises specifically, accumulating moderate scores only for the government as these systems will benefit or be affected by the implementations discussed. Nonprofits may find relevance as well through potential involvement in the consortium mentioned, but again that is indirect. There is no significant coverage of the Academic sector, nor is it addressing international cooperation directly, allowing for lower scores in those areas.

The text explicitly pertains to the implications of AI on user experience, particularly highlighting the potential inaccuracies and appropriateness of outputs generated by generative AI systems. This aligns closely with Social Impact, as it addresses user protection and the information users receive when interacting with AI systems. Data Governance is also relevant since the warning implies a management responsibility towards data outputs generated by these systems. System Integrity might be relevant as it pertains to the transparency of the AI's outputs and oversight, while Robustness is less applicable since the focus doesn’t primarily deal with performance metrics or benchmarks. In summary, the legislation is significantly concerned with how AI impacts users and society, particularly in managing expectations and providing warnings regarding generated outputs. This raises vital issues around accountability and the ethical use of AI, particularly generative models and their content creation capabilities.

The legislation relates most closely to Government Agencies and Public Services as it involves the requirement for system operators to provide warnings about AI outputs, which is a public service to ensure safe interaction with AI technology. There are also implications for Private Enterprises, Labor, and Employment, as businesses deploying generative AI systems will be affected by these regulations regarding consumer safety. However, it does not clearly pertain to the other sectors like Politics and Elections, Healthcare, or the Judicial System. In consideration of the specific context provided, this legislation is primarily directed at enhancing user knowledge and safety in public and business environments.

The text explicitly addresses artificial intelligence within the context of regulating automated decision systems (ADS), indicating a clear focus on social impacts, data governance, system integrity, and robustness. AI is at the core of this legislation as it aims to regulate how automated systems interact with and impact individuals in the workplace, ensuring fairness, accountability, and transparency. This connects directly to Social Impact, as it deals with impacts on individuals and discrimination concerns, as well as Data Governance due to its focus on proper handling and management of data used by these AI systems. Additionally, it touches on aspects of System Integrity by emphasizing transparency and oversight in automated decisions and Robustness through the need for audits and compliance with established benchmarks.

The legislation primarily impacts the workplace, which aligns it closely with Private Enterprises, Labor, and Employment as it regulates how AI affects employment decisions. It discusses the use of AI in hiring, monitoring, and managing employee data, directly addressing concerns for workers' rights and employer responsibilities. It also relates to Government Agencies and Public Services in terms of compliance and oversight by government entities involved in enforcing these regulations. Although it does touch on technological aspects applicable to healthcare or academia, the direct focus is on employment and regulatory processes in business contexts, giving it high relevancy ostensibly in the private sector.

This text outlines the creation of an ethics commission specific to AI in New York, which addresses ethical use, compliance reviews, educational resources, and legislative advisement on AI issues. The relevance of each category is as follows: 1. Social Impact is extremely relevant due to the focus on unethical AI practices, discrimination, misinformation, and the impact of AI on protected characteristics. 2. Data Governance is very relevant as the bill addresses unauthorized data collection, processing, and the need for consents, which ties directly to data governance. 3. System Integrity is relevant as the commission can impose penalties related to maintaining integrity and ethical standards in AI systems. 4. Robustness receives a moderate score, as while there is mention of audits and certifications, the primary focus is more on ethical compliance than performance benchmarks. Overall, the relevance of the text to the categories is substantial due to the ethical implications outlined within the bill, making clear connections to contemporary concerns surrounding AI utilization.

The text involves the regulation of AI across various sectors, particularly within state agencies and private companies operating in New York. The relevance of each sector is as follows: 1. Politics and Elections is slightly relevant since the bill may indirectly affect the political space through ethical considerations. 2. Government Agencies and Public Services is extremely relevant as the commission oversees AI used within state agencies and public service applications. 3. Judicial System does not have direct relevance, as the bill does not address legal or judicial AI issues explicitly. 4. Healthcare and Private Enterprises, Labor, and Employment are also not directly relevant, though implications on ethical practices might indirectly influence these sectors. 5. Academic and Research Institutions is slightly relevant due to potential oversight of AI research ethics. 6. International Cooperation and Standards, Nonprofits and NGOs, and Hybrid, Emerging, and Unclassified receive low relevance as these aspects aren't addressed specifically in the text. Overall, the most pertinent sector is the Government Agencies and Public Services due to the direct oversight and ethical governance of AI applications in this context.

The text specifically addresses the implications of AI technology, particularly chatbots, and their potential impact on society by imposing liabilities for damages caused by their misuse. This aligns with the Social Impact category as it relates to holding developers accountable and consumer protections against AI-driven harm or misinformation. The provisions mandate that chatbots provide clear communication about their nature, which helps safeguard individuals from misunderstandings that could lead to harm. The emphasis on liability for chatbots that impersonate licensed professionals further underscores the serious societal repercussions of AI errors and misrepresentations. Therefore, this category appears very relevant. The Data Governance category is also relevant, but less so, as the text refers broadly to accountability rather than specific data governance issues; it does touch on user notification but lacks in-depth exploration of data handling. In terms of System Integrity, the text does not address security, interoperability, or human oversight in AI systems directly. The aspect of liability ties in with ensuring a certain level of robust conduct, but this is more tangential than fundamental. Overall, the Social Impact is rated highest due to direct implications for individuals and society, followed by Data Governance which is moderately relevant, while System Integrity and Robustness are less applicable.

The text predominantly concerns the implications of AI technology, particularly chatbots, which are relevant to several sectors. In the context of Healthcare, while some licensed professionals may relate to medical fields, the text itself does not directly pertain to health-related AI applications. The Government Agencies and Public Services sector may be partially relevant given that the legislation reflects on AI usage, but it does not specifically address governmental operation. The Judicial System is moderately relevant, as it involves legal accountability and could pertain to misrepresentation through chatbots in legal contexts. Private Enterprises are particularly relevant since the liable parties discussed are businesses that deploy chatbots; this fits within corporate governance and operational compliance. Academic and Research Institutions, International Cooperation and Standards, and Nonprofits and NGOs are less relevant as they don't directly relate to the core provisions outlined in the text. Overall, Private Enterprises receives the highest score, followed by the Judicial System, with the others less significantly connected.

The text includes substantial references to 'Artificial Intelligence' and 'Machine Learning,' explicitly defining these terms and discussing their implications in the context of polluting atmospheric interventions. The bill appears to address AI's role in potentially hazardous activities, indicating relevance to categories surrounding societal impact, data governance, and system integrity. However, the specific focus on atmospheric interventions and cloud seeding may limit its connection to broader AI categories, particularly Robustness. Overall, the social implications of AI’s involvement in atmospheric manipulation are of significant concern, making Social Impact the most relevant category. The text does not delve deeply into data governance or system integrity concerning AI specifically, as it primarily centers on prohibitive actions against atmospheric interventions rather than on data handling or AI system security.

While the text touches upon the application of AI in atmospheric interventions and pollution, it chiefly focuses on the implications for environmental safety and governance rather than a specific sector like healthcare or judicial systems. The bill could have implications for government oversight due to its reference to the Pennsylvania State Police and sheriffs enforcing these regulations. However, it lacks a direct connection to the typical legislative frameworks usually encountered in sectors like healthcare or politics. Given the focus on AI's involvement in environmental contexts, the connection to Government Agencies, particularly concerning enforcement, is the most pertinent, but other sectors like Healthcare, Private Enterprises, and Nonprofits do not directly relate to the content of this bill.