How It Works
How CNTR AISLE structures, scores, and profiles AI legislation, from governance dimensions to the spider graph on each bill profile.
Dimensions
Every bill is scored across seven governance dimensions. Expand one to see its key questions.
Risk identification and mitigation, lifecycle of impact assessments, documentation and transparency, auditing and compliance, precautionary measures, and licensing.
Key Questions
- Does the bill require covered entities (as defined in the bill) to conduct Impact/Risk Assessment (IA/RA) or similar evaluations?
- Does the bill provide compensation and civil recourse for those affected by harms?
- If the bill includes auditing requirements or you select 'Yes' for A28, does the bill define how frequently auditing should occur (i.e., single point or regular intervals)?
- Does the bill impose explicit bans on AI systems, such as preventing deployment due to safety risks or requiring compliance before use?
- Does the bill require tools of resilience, e.g., kill switches, recalls, emergency training and protocols, or establishing thresholds at which a deployed system should be shut down?
Methodology
The key questions were selected to represent the longer set of scoring questions by identifying provisions that most clearly reflect the practical impact and structure of a bill within each dimension.
This process was led by our Head of Policy, Tomo Lazovich, who reviewed each module and determined which questions most meaningfully represent the breadth of that category. The goal was not to reduce complexity, but to ensure that the profile reflects the core regulatory posture of a bill while remaining readable and comparable across jurisdictions.
Because each module contains many detailed questions, we needed a way to surface high-level indicators on the bill profile without overwhelming the reader. The selected questions were those that:
Capture concrete requirements such as impact assessments, sensitive data handling, monitoring obligations, and more
Signal enforceability such as private right of action, civil remedies, platform liability, and more
Indicate structural mechanisms such as bans, auditing frequency, institutional design, and more
Scoring Questions
The complete set of questions used to score each bill, organized by dimension.
Reading Profiles
The Spider Graph
Each axis represents one governance dimension (e.g., Accountability, Data Protection, Bias, AI & Education, Synthetic Content).
- A farther distance from the center indicates greater policy coverage, specificity, or enforcement strength in that dimension.
- A closer point to the center indicates limited coverage, vague language, or absence of enforceable mechanisms.
The overall shape reflects the bill's governance profile — whether it is balanced across dimensions or concentrated in specific areas. A more circular, evenly distributed shape suggests comprehensive coverage, while sharp spikes indicate targeted regulation in select domains.
Limitations: What Does "Zero" Mean?
A score of zero does not necessarily mean "no governance." It may indicate:
- The issue is addressed under a different legal framework not captured in this dimension.
- The bill references existing statutes rather than introducing new provisions.
- The language is too vague or indirect to meet the scoring threshold.
- The bill intentionally limits scope to a specific domain (e.g., only election deepfakes).
Therefore, a zero reflects absence within this scoring framework, not necessarily absence of regulation in the broader legal ecosystem.
Example
US S 5152
Artificial Intelligence Civil Rights Act of 2024
Areas of Impact: Proportion of Questions Answered Yes Per Category
What's Next
Broadening Our Reach
As AISLE enters its public launch phase, our next focus is expanding its visibility and impact. With the official website live, we are working to ensure the platform reaches policymakers, journalists, researchers, and members of the public who are actively engaging with AI governance. Our goal is not simply to host legislative information, but to provide structured, comparative, and interpretable analysis of AI-related bills across states.
Expanding the Analytical Framework
On the policy front, we are expanding and refining our analytical framework to reflect the rapidly evolving legislative landscape. As states experiment with different approaches to AI governance, from synthetic content regulation to education oversight and transparency mandates, we are continuously assessing whether our scoring dimensions capture the most meaningful areas of regulatory impact.
NLP & LLM Integration
In parallel, we are advancing research and experimentation in NLP and large language model (LLM) integration. We are exploring how AI-assisted tools, such as automated bill summaries, similarity analysis, and stakeholder-perspective modeling, can responsibly enhance legislative interpretation while maintaining transparency and human oversight.