Analyzing the Passage of State-Level AI Bills
Artificial intelligence (AI) continues to dominate headlines, spanning chip supremacy and job losses to AI actresses and U.S. national security. These articles demonstrate that AI is top of mind across industries, and the technology is touching virtually every aspect of government and society.
This study builds on the notion that, as with countries, not all U.S. states are equally capable of implementing AI, and important differences exist. In recent analyses, it was found that bills proposing to ban nonconsensual intimate imagery (NCII) and child sexual abuse material (CSAM) had the greatest number of proposals, yet none had become law at the time of review. Employment was the only area where a significant percentage of bills had been signed into law.
Characteristics of States Proposing AI Legislation
Younger, wealthier, and Democratic-leaning states are leading the way in AI legislation. In contrast, older, poorer, and Republican-leaning states show less activity in proposing AI bills. This discrepancy suggests that these states may be either ideologically opposed to action or lack the necessary capacity to act.
Success Rates of AI Bills
According to data from the Brookings Center for Technology Innovation, a total of 386 AI-related bills were introduced across all 50 states, with at least one bill in each state. These bills were grouped into three overarching themes:
- Protection of Individuals: While this theme had the highest percentage of active bills, it also recorded the lowest percentage of bills passed. Legislators face challenges in addressing fairness, especially in areas like healthcare decision-making.
- Transparency and Trust: This theme had the largest number of bills overall, with a high percentage of active bills (80%). However, only 15.5% of these bills were passed, indicating that legislators struggle to balance competing interests such as disclosure versus transparency.
- Responsible Governance: Comprising the smallest number of bills (114), this theme had the highest passage rate (38.6%) among the three. These bills often reflect standard institution-building proposals that receive broad bipartisan support.
Predicting Bill Passage
To better predict which kinds of bills are likely to pass or fail, the analysis used several indicators, including per capita income, poverty rates, human capital, business profile, governor party affiliation, and state party base. A qualitative comparative analysis (QCA) was conducted to examine how these conditions combine to influence bill passage.
By utilizing the principle of asymmetry in QCA, the study offers insights into the absence of passed and failed bills by analyzing active bills. This approach allows for a more nuanced understanding of the dynamics surrounding AI legislation.
In conclusion, the passage of AI legislation varies significantly across states, influenced by socio-economic factors and political ideologies. Understanding these elements is crucial for policymakers seeking to navigate the complex landscape of AI development effectively.
