FIFTY STATES, FIFTY ALGORITHMS: STATE-LEVEL VARIATION IN AI GOVERNANCE AND ITS IMPLICATIONS FOR PUBLIC TRUST.

The United States does not currently have one comprehensive federal law governing the use of artificial intelligence. As a result, individual states have begun developing their own AI laws and policies which differ considerably. Some states such as Colorado and California have established relatively broad requirements concerning discrimination, transparency, risk assessment, human oversight and accountability. Other states have adopted narrower rules, focused mainly on particular technologies or policy areas, while many states have no AI-specific laws in several important sectors.

This paper examines how these differences create uneven protections for people across the United States. Using a theory-informed comparative analysis of state AI legislation, the study draws on a 50-state legislative tracking dataset covering AI and algorithm-related measures in employment, healthcare, education, credit and lending, criminal justice, government use, and elections. Each state’s approach is examined according to whether its laws provide safeguards such as notice that AI is being used, explanation or transparency, risk assessment, human review, accountability, and opportunities to question or appeal an AI-assisted decision. The analysis also distinguishes between enacted laws, proposed legislation, government policies, local rules, and states where no AI specific measure was identified.

The study does not directly measure public trust through surveys or other individual-level data. Instead, it draws on theories of procedural justice, institutional trust, and policy feedback to examine the likely implications of these unequal protections. The theories suggest that people are more likely to regard government institutions as fair and legitimate when decisions are made through consistent, transparent and accountable procedures. When people facing similar AI-assisted decisions receive different protection simply because they live in different states the resulting inequality may raise concerns about fairness and equal treatment.

The analysis shows that the United States is developing multiple state systems of AI governance rather than one consistent national framework. Of the 350 state-sector observations examined, 108 contained enacted laws or executive and administrative measures, 109 involved other identified legislative measures, while 133 had no qualifying AI-specific measure identified. Residents of some states therefore receive stronger procedural protections against algorithmic harm, while residents of other states must rely mainly on general civil-rights, privacy, consumer protection, or administrative laws. This uneven system does not by itself prove the public trust has declined, but it does however create conditions that theories of procedural justice associate with concerns about fairness, and institutional legitimacy.

The paper contributes to debate about American federalism, AI, accountability and equitable public policy. It considers whether state-led regulation can provide adequate and reasonably consistent protection while allowing policy experimentation and technological innovation in the absence of comprehensive federal legislation.