AN ACT to amend Tennessee Code Annotated, Title 4, Chapter 3, Part 31, relative to the artificial intelligence advisory council.
SB1362 revises Tennessee’s artificial intelligence advisory council law. It updates the statutory definition of “artificial intelligence” to a more detailed, machine-based definition that describes AI systems as using human-defined objectives, perception, automated analysis, and model inference to influence real or virtual environments. The bill also expands the council’s membership range from exactly 24 members to between 24 and 27 members and allows two or three nonvoting expert members to participate. In addition, it requires that at least two governor-appointed members have demonstrated AI research or development experience, and it authorizes the council to invite additional private-sector or academic experts to advise or participate in subcommittees.
The bill further directs the council to incorporate data privacy and security best practices into its recommended action plan and to align those recommendations with the state’s enterprise artificial intelligence policy. It also requires the council, beginning no later than December 31, 2025 and annually thereafter, to compile an inventory of federal and state laws, regulations, and executive orders affecting AI development or use in Tennessee. That inventory must identify overlaps, conflicts, and gaps; recommend revisions or clarifications to reduce compliance burdens; and assess alignment with federal directives and recognized industry frameworks. The act takes effect July 1, 2025.
SB1362 would amend Tennessee Code Annotated Title 4, Chapter 3, Part 31, changing the structure and duties of the Tennessee artificial intelligence advisory council. It affects the statutory definition of AI, council membership and appointment requirements, and the council’s reporting obligations. The bill does not create direct regulation of private AI developers, but it expands the council’s role in advising state government on AI policy, privacy, security, and legal harmonization, which could influence future agency rules and legislation affecting state agencies and AI-related stakeholders.
Based on the bill text and the absence of recorded committee debate or votes in the provided materials, the measure appears to be framed as a technical and policy-oriented update rather than a controversial regulatory overhaul. Its emphasis on expert participation, privacy and security, and legal inventorying suggests a generally pragmatic approach to AI governance. No opposing viewpoints are documented in the provided context, so the overall sentiment cannot be measured from debate history, but the bill’s structure indicates an effort to strengthen state coordination around AI rather than restrict it.
The main potential points of contention are likely to be the scope of the advisory council’s authority, the requirement to inventory and evaluate existing laws for conflicts or gaps, and the emphasis on aligning recommendations with the state’s enterprise AI policy. Stakeholders concerned about regulatory burden may view the annual legal inventory and harmonization review as a step toward future oversight, while supporters may see it as necessary to avoid fragmented or inconsistent AI rules. Another possible issue is the requirement that certain appointees have AI expertise, which could affect appointment flexibility, though no specific objections are recorded in the provided materials.