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About these categories

Social Impact: encompasses legislation that specifically addresses the impact of AI on society and individuals, including but not limited to; reducing the carbon footprint of AI systems, holding system developers accountable for the outputs of these systems, the establishment of new fairness and bias metrics to guard against AI-driven discrimination, the use of AI by minors, consumer protections for AI products, regulations that address psychological, physical, or material harm caused by interactions with AI systems, and the role of AI in misinformation, public discourse, and the erosion of trust in public institutions.

Data Governance: encompasses legislation that specifically addresses the secure and accurate collection and management of data within AI systems, including but not limited to; mandates for rectifying inaccuracies in AI data sets, mandates for ensuring AI data sets are free from biases, regulations that address intellectual property concerns with the unauthorized use of protected material in AI data sets, and requirements for data encryption, data anonymization, controlled access, and compliance with consumer privacy laws.

System Integrity: encompasses legislation that specifically addresses the inherent security, transparency, and control of AI systems, including but not limited to; mandates for human intervention and oversight in AI processes, standards for the interoperability of AI systems in essential services, and the implementation of security measures such as threat modeling, penetration testing, and incident response.

Robustness: encompasses legislation that specifically addresses the development and adoption of new benchmarks for AI performance, including but not limited to; the certification of AI systems against new benchmarks, ensuring compliance with international standards, requirements for auditing and transparent reporting to verify continuous regulatory compliance, and the creation of specialized oversight bodies.

About these sectors

Politics and Elections: encompasses legislation that specifically addresses the use and regulation of AI in political campaigns, electoral processes, and related legislative activities.

Government Agencies and Public Services: encompasses legislation that specifically addresses the use and regulation of AI by state and federal government agencies, as well as AI applications in the delivery of public services and enhancing government operations.

Judicial System: encompasses legislation that specifically addresses the use and regulation of AI by judicial and legal systems, including but not limited to the use of AI in case management and legal decision making.

Healthcare: encompasses legislation that specifically addresses the use and regulation of AI in healthcare settings, including but not limited to AI applications within hospitals and clinics, the development of AI-enabled diagnostic tools and related medical technologies, and the management of medical data.

Private Enterprises, Labor, and Employment: encompasses legislation that specifically addresses the use and regulation of AI in business environments, ensuring fair competition, and the effects of AI on labor markets, employment practices, and corporate governance.

Academic and Research Institutions: encompasses legislation that specifically addresses the use and regulation of AI in educational and research contexts.

International Cooperation and Standards: encompasses laws and agreements that address international cooperation on AI development, use, and regulation, including but not limited to multinational standards, cross-border data practices, and global ethical guidelines for AI.

Nonprofits and NGOs: encompasses legislation that specifically addresses the use and regulation of AI by nonprofits and non-governmental organizations and institutions.

Hybrid, Emerging, and Unclassified: encompasses legislation that doesn't neatly fit into other categories, as well as the application of AI in hybrid or emerging sectors.

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4162 results:


Hawaii HB1949: Relating To The Generation Of Wildfire Susceptibility Maps For Hawaii.

Description: Requires the University of Hawaii to establish and implement a two-year program to develop web-GIS wildfire susceptibility and vulnerability maps for the State of Hawaii to determine which communities, landscapes, buildings, and infrastructure are most vulnerable to future wildfires. Declares that the general fund expenditure ceiling is exceeded. Makes an appropriation. Effective 7/1/3000. (SD1)
Collection: Legislation
Status date: March 5, 2024
Status: Engrossed
Primary sponsor: Linda Ichiyama (2 total sponsors)
Last action: Report adopted; Passed Second Reading, as amended (SD 1) and referred to WAM. (March 22, 2024)

Category: None (see reasoning)

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.


Sector:
Government Agencies and Public Services (see reasoning)

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.


Keywords (occurrence): artificial intelligence (1)

South Carolina H4660: Deceptive and fraudulent deepfake media in elections

Description: Amend The South Carolina Code Of Laws By Adding Section 7-25-230 So As To Prohibit The Distribution Of Deceptive And Fraudulent Deepfake Media Of A Candidate Within Ninety Days Of An Election Unless The Media Includes Requisite Disclosure Language, And To Authorize A Candidate Whose Likeness Is Depicted In Media Distributed In Violation Of This Section To Seek Injunctive Or Other Equitable Relief As Well As An Action For Damages Against The Distributor Of The Media.
Collection: Legislation
Status date: Jan. 9, 2024
Status: Introduced
Primary sponsor: Jermaine Johnson (2 total sponsors)
Last action: Referred to Committee on Judiciary (Jan. 9, 2024)

Category:
Societal Impact
Data Governance (see reasoning)

The text is highly relevant to the Social Impact category as it directly addresses the potential harms caused by deceptive and fraudulent deepfake media in the context of elections. The bill aims to protect candidates from media that could damage their reputation or mislead voters, which is a significant societal concern. Issues of accountability and misinformation are clearly articulated through the regulation of synthetic media. Although the bill touches on aspects of governance and data usage, the primary focus remains on the societal implications of deepfakes, particularly in an electoral setting. The relevance to Data Governance is moderate, given the use of terms such as ‘synthetic media’ and the regulations surrounding the disclosure within media, indicating some implications for data management within these technologies. However, the law primarily targets the social ramifications rather than the governance of the data itself. System Integrity and Robustness have lesser relevance because while the legislation seeks to regulate content and ensure disclosures, it does not address core system integrity issues like cybersecurity or the performance benchmarks of AI systems involved in the creation of deepfakes. Therefore, while there are connections, they are peripheral rather than central to the text.


Sector:
Politics and Elections
Judicial system (see reasoning)

The text explicitly discusses the implications of AI-generated media (


Keywords (occurrence): artificial intelligence (1) deepfake (9) synthetic media (3) show keywords in context
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