AI regulation
Approaches for incorporating public interest technology principles into state-level AI regulatory agendas and procurement rules.
A practical, field-tested guide to embedding public interest technology principles within state AI regulatory agendas and procurement processes, balancing innovation with safety, fairness, accountability, and transparency for all stakeholders.
July 19, 2025 - 3 min Read
As state governments confront rapid advances in artificial intelligence, they face a dual imperative: encourage responsible innovation while protecting residents from harms that can arise when systems scale without safeguards. Public interest technology principles offer a compass for aligning policy goals with real-world outcomes. These principles emphasize transparency about how algorithms operate, fairness in how decisions affect diverse communities, accountability for who bears responsibility when mistakes occur, and opportunities for public participation in rulemaking. By codifying these aims into the regulatory fabric, states can create regulatory pathways that are proactive rather than reactive, enabling sustainable deployment of AI across critical sectors.
A concrete way to operationalize public interest principles is to integrate them into the earliest stages of policy design, long before procurement contracts are drafted. This requires cross-agency collaboration to define baseline standards for data quality, model explainability, and performance monitoring. States can establish dashboards that publicly report key indicators such as bias audits, error rates across demographic groups, and incident response timelines. Integrating human-centered design perspectives helps ensure that technological choices reflect lived experiences, not just technical feasibility. When regulators treat public input as a core input rather than a checkbox, the resulting rules are more attuned to community needs and more durable across political shifts.
Data stewardship and equitable outcomes in practice.
Procurement rules must reflect public interest priorities in a way that is both rigorous and accessible to vendors of all sizes. This means requiring vendors to disclose training data provenance, model limitations, and safety containment measures in plain language, while also mandating independent validation by third parties. States can adopt modular procurement frameworks that require baseline capabilities and allow additional, value-added features through competitive bidding. Importantly, requirements should be adaptable to evolving technologies, with sunset clauses and periodic re-evaluations built into contracts. By anchoring procurement to public interest outcomes, states avoid lock-in to single vendors and preserve competitive markets that foster safer, more transparent AI.
Accountability structures are essential when deploying AI at scale. To achieve this, states should impose clear responsibility lines for developers, operators, and end-users, backed by remedies such as post-market surveillance, redress mechanisms, and accessible complaint channels. Regulators can require incident reporting that classifies harms by affected populations, enabling trend analysis and targeted corrective actions. Independent audit regimes, including code reviews and data governance assessments, help deter corner-cutting and ensure ongoing compliance. A transparent registry of regulated entities, including performance metrics and enforcement histories, creates a public accountability layer that strengthens trust and accelerates learning across agencies.
Engagement with communities and workers in governance.
Data governance sits at the heart of public-interest AI. States should mandate rigorous data minimization, consent where appropriate, and robust privacy protections aligned with citizens’ expectations. Equally critical is the inclusion of diverse datasets to mitigate biases that misrepresent communities. Governance should require ongoing bias evaluation with auditable methodologies, documenting how data choices influence outcomes. In procurement terms, bidders must demonstrate strategies for data lifecycle management, leverage privacy-preserving techniques, and outline safeguards against misuse. By building a culture of responsible data stewardship into the procurement criteria, states set a standard that reduces risk while supporting legitimate uses of AI across public services.
Transparent accountability hinges on open communication about model behavior. Regulators can require disclosures about model purpose, capability limitations, and the scope of decision-making autonomy. Explainability should be defined not as a single metric but as a spectrum of disclosures tailored to different stakeholders, from frontline workers to the general public. State policies can promote version control, change tracking, and traceability of outputs to source data and trained models. The procurement process benefits when bidders articulate how explanations will be delivered to users in practical terms, including multilingual formats and accessible interfaces. Such practices foster informed consent and empower communities to participate meaningfully in governance.
Safeguards, standards, and adaptive governance.
Engaging communities meaningfully requires deliberate outreach, not perfunctory consultation. States can establish steering groups that include residents, civil society organizations, industry representatives, and subject-matter experts to co-create standards. Public hearings, citizen juries, and participatory testing sessions help surface concerns early and illuminate unintended consequences. For procurement, feedback loops tied to pilot deployments enable evaluable learning before full-scale rollout. Workers who operate AI-enabled systems must have channels to report safety concerns, training needs, and operational challenges. When governance reflects diverse voices, policies emerge that better protect vulnerable populations and adapt to regional contexts.
In addition to formal processes, education and capacity building strengthen public-interest outcomes. Regulators can offer training programs that demystify AI concepts, explain risk assessment procedures, and illustrate how governance mechanisms operate in practice. Providing interpretive materials at appropriate literacy levels helps ensure accessibility for nonexperts who are affected by AI decisions. States can encourage academic partnerships to study ethical deployment patterns and publish lessons learned. Procurement rules should incentivize vendors who invest in upskilling public-sector staff, creating a virtuous cycle where better understanding of technology leads to more robust safeguards and more effective delivery of services.
Implementation pathways and measurable outcomes.
Adaptive governance recognizes that AI landscapes evolve rapidly. States should embed mechanisms for continuous monitoring, regular risk re-assessments, and updates to standards as new evidence emerges. This approach reduces regulatory drift and aligns policy with current capabilities. Procurement rules can include staged check-ins, re-bid opportunities for improved solutions, and contingency plans for discontinuing problematic deployments. Standards must be technology-agnostic where possible, focusing on outcomes rather than specific tools. By prioritizing resilience and adaptability, governments can maintain public confidence while fostering responsible innovation across sectors such as health, transportation, and law enforcement.
A practical framework for adaptive governance combines risk-based tiers with proportional safeguards. High-risk applications may require stringent validation, independent audits, and robust oversight, while lower-risk deployments could rely on streamlined processes that still enforce basic accountability. Regulators should publish guidance on when and how to escalate concerns, ensuring clarity for both developers and users. Procurement should reflect risk-aware pricing and contract terms that fund ongoing monitoring rather than one-off checks. By aligning resource allocation with risk, states can deliver safer AI services without stifling beneficial experimentation and improvement.
Implementation begins with a clear policy architecture that maps public-interest principles to concrete rules, timelines, and responsibilities. States should publish a roadmap detailing the steps from concept to procurement to deployment, including milestone-based evaluations. Measuring success requires robust indicators such as accessibility of services, reduction in disparate outcomes, and the speed of remediation after issues arise. Transparent reporting on these metrics helps build public trust and demonstrates accountability. As jurisdictions learn from early pilots, they can scale best practices to neighboring states, creating a broader ecosystem of responsible AI governance that benefits all residents.
A final ingredient is political will paired with practical resources. Regulators need budgetary support, technical expertise, and robust stakeholder networks to sustain public-interest objectives. The procurement framework should reward vendors who demonstrate meaningful public engagement, ethical data handling, and trackable impact improvements. By weaving public interest principles into the fabric of regulation and procurement, states can cultivate AI ecosystems that are innovative, inclusive, and safe. The enduring value lies in governance that evolves with technology, protects rights, and delivers tangible benefits to communities across diverse contexts.
