Texas Responsible AI Governance Act: Analysis

In part I of this series on the Texas Responsible AI Governance Act (TRAIGA), we conducted a detailed review and breakdown of the act’s core provisions. Here, we’ll examine TRAIGA from a critical perspective, identifying its key strengths and weaknesses, anticipating its impacts, and providing suggestions regarding potential future avenues for improvement.

However, before we get started, it’s important to understand why TRAIGA is positioned as legislation that, now enacted, could deeply influence the form, function, and scope of the US federal AI governance agenda. The reasons we’ll shortly examine are by no means exhaustive, but they do showcase, at a high level, why we would be wise to conceptualize TRAIGA’s enactment as a nationally significant event.

First, the act has already inspired major controversy and harsh criticism from notable members of the AI community. While the sentiments behind these criticisms are valuable in their own right, what matters most in this context is quite literally the controversy itself. If the AI community didn’t have sufficient reason to believe that TRAIGA would materially and substantially affect the course of current and future AI innovation outside of Texas, the incentive to publicly support or oppose it would dissolve.

Second, it’s not uncommon for state-level regulations to set federal precedents, and even more so, cement the foundation for federal regulations. For instance, Massachusetts' 2006 healthcare reform initiative—known as “Romneycare”—became the blueprint for Obama’s Affordable Care Act. Similarly, the California Consumer Privacy Act (CCPA), passed in 2018, clearly serves as the main source of inspiration behind the American Data Privacy and Protection Act. It’s also worth noting that the CCPA was modeled on the EU’s GDPR, which further highlights how seemingly localized regulations can generate large-scale regulatory impacts, even on a global scale.

Third, while California is the US' modern-day epicenter for technological innovation, other states are beginning to attract the attention of high-profile tech innovators, with Texas standing out as an early contender. In other words, there is a clear implication at play here: if Texas, given its newfound status as a blossoming tech innovation hub, develops a comprehensive technology-centric regulation, we should take it seriously. To this point, President Trump’s Project Stargate, a relatively recently announced $500 billion private investment initiative in national AI infrastructure, will commence with the construction of a data center in Texas.

Fourth, two key components of TRAIGA — the establishment of the Texas AI Council and the broad definition of AI systems — if applied at the scale of the federal government, could dramatically expand the scope of control federal regulators have over how AI systems are developed and deployed nationally. This isn’t to say that our government implicitly craves tight control over innovative technologies; in fact, historically loose tech regulation appears to directly contradict this point. However, we must entertain the possibility that AI’s potential impacts on critical domains like national security, military operations, and public health and infrastructure, will inspire a regulatory incentive structure that favors much tighter controls at the national scale.

Now that we’ve broadly explained why we expect that TRAIGA’s influence will range beyond Texas, we can dive into the heart of our discussion, taking a deeper look at the act’s key strengths, weaknesses, and related impacts.

Strengths, Weaknesses, and Impacts

When making the following value-based categorizations regarding TRAIGA’s strengths and weaknesses, we are doing so from a predominantly regulatory perspective (i.e., that of a governing body responsible for implementing, maintaining, enforcing, and/or modifying and updating compliance requirements). This is an important point to consider, seeing as whether something classifies as a strength or weakness often depends on the frame of reference one adopts. This point also highlights one of the central reasons for why developing regulation, especially for an exponential technology like AI, is so challenging.

Once we’ve examined TRAIGA’s strong and weak points, we’ll venture into uncertainty, predicting a variety of real-world impacts we expect the act will produce. However, in the interest of fostering a more nuanced and expansive interpretation of these future trajectories, we’ll subdivide impacts into a selection of groups: 1) regulatory, compliance, and legal 2) economic, 3) technological, 4) social and ethical, and 5) long-term.

Strengths

TRAIGA exhibits a comprehensive regulatory scope whereby concrete standards for ethical AI development and deployment, consumer protections, and safe innovation practices are established. In terms of key strengths, TRAIGA:

• Covers a wide range of technical and non-technical AI governance issues throughout key stages of the AI lifecycle, fostering regulatory and AI risk management preparedness in the face of potential AI advancements.
• Establishes clear prohibitions against harmful AI uses, including manipulation, discrimination, and misuse of biometric data, creating explicit boundaries for AI development and deployment.
• Mandates, via disclosure requirements and transparency provisions for governmental entities, governmental practices that ultimately aim to bolster public trust, safety, and proactive, as opposed to reactive, AI risk and impact management.
• Focuses on preventing specific harmful AI practices across all systems, rather than creating complex risk categorization frameworks, which simplifies compliance, enforcement, and potentially even oversight.
• Outlines robust consumer protections, ensuring that consumers know when they’re interacting with governmental AI systems and have recourse (i.e., the ability to submit complaints) through the Attorney General’s complaint mechanism.
• Assigns distinct responsibilities to developers and deployers, ensuring that accountability standards are applied throughout the AI lifecycle and that responsibility-based ambiguities are minimized through clear categorization.
• Balances the need for AI innovation with safety by affording eligible regulatory sandbox applicants the ability to conduct controlled pre-deployment safety testing of their AI systems while being temporarily exempt from compliance requirements.
• Establishes local preemption to ensure consistent AI governance standards across Texas, preventing a patchwork of conflicting or inconsistent municipal regulations.
• Creates the Texas AI Council as the central advisory body regarding all things AI within the state of Texas, laying the groundwork for a state-level AI governance structure and strategy that remains robust, forward-looking, agile, and transparent.

Weaknesses

Notwithstanding its comprehensive scope, TRAIGA is far from perfect, omitting important details throughout several areas, failing to provide tangible clarification on ambiguous requirements or concepts, excluding certain AI systems known to pose substantial risks, and supporting an AI governance approach that may prove challenging to implement effectively. TRAIGA’s key weaknesses could be summarized as follows:

• Explicitly prohibits certain AI uses like social scoring and unlawful biometric surveillance, focusing on specific AI use cases when such practices, independent of AI, should be banned as a whole. This creates a massive regulatory loophole.
• Does not afford explicit exemptions for small businesses, potentially creating compliance challenges for smaller entities with limited resources. This phenomenon has materialized before, most notably, during the early stages of GDPR compliance in the EU.
• Does not provide detailed descriptions of regulatory sandbox provisions, including specific eligibility criteria, evaluation metrics, and enforcement mechanisms, increasing the possibility of inconsistent oversight and/or potentially limited engagement.
• Broadly defines AI systems without creating risk-based categorizations, which could lead to over-inclusive enforcement or confusion about which systems require the most regulatory attention.
• Primarily focuses on governmental use of AI for certain requirements (like disclosure and biometric data), leaving private sector AI use less regulated in key areas. This could also elevate regulatory capture risks for particularly powerful technology companies.
• Fails to specifically address general-purpose AI systems and AI agents, both of which represent the AI frontier and inspire a conglomeration of interconnected short and long-term risks, benefits, and impacts that can manifest at vastly different scales across multiple domains and timelines.
• Provides a 60-day cure period for violations, which, while reasonable for some issues, would likely be insufficient for complex AI system modifications.
• Affords the Texas AI Council limited powers as an advisory body without rulemaking authority, potentially constraining its ability to respond effectively to rapidly evolving AI challenges while granting the Attorney General exclusive enforcement authority, which could create bottlenecks in enforcement actions.

Impacts

Regulatory, Compliance & Legal

• Developers and deployers will need to ensure their systems don’t violate the act’s prohibitions, requiring careful system design, testing, and post-deployment practices. The apparently broad definition of AI systems indicates that many entities may need to assess whether they fall under TRAIGA’s scope.
• Governmental entities will face immediate compliance burdens for disclosure requirements when using AI systems that interact with consumers, necessitating system updates and communication protocols. This could be a major hurdle for agencies that still utilize outdated communication protocols and legacy systems or infrastructures.
• Private sector entities will primarily need to ensure their AI systems don’t engage in prohibited practices like discrimination, manipulation, or unauthorized biometric identification. Compliance burdens could become significant in this context, especially for major AI providers.
• Core oversight bodies (i.e., the Texas AI Council and Attorney General) will need to quickly establish effective operational procedures, with the AG’s office creating the online complaint mechanism and developing investigation protocols. The Council will need to define its advisory role, build in sufficient multidisciplinarity, and establish working relationships with state agencies.
• The 60-day cure period and civil penalty structure ($10,000-$200,000 per violation) will incentivize proactive compliance, though it’s important to reiterate that the broad scope of covered AI systems may create uncertainty about compliance obligations, particularly as AI advances.

Economic

• Compliance costs may vary significantly depending on whether entities are governmental or private sector, with governmental entities facing more explicit and stringent requirements around disclosure and biometric data use.
• Regulatory sandboxes could be moderately effective in promoting economic growth while maintaining innovation safety. However, the lack of detailed sandbox provisions could perpetuate entry and exit barriers that introduce unexpected inefficiencies and continued oversight and validation challenges. As formulated, TRAIGA’s regulatory sandbox program invites pre-deployment testing without providing concrete testing protocols.
• Market differentiation dynamics may emerge, favoring compliant companies who will market themselves as trustworthy and ethical AI leaders to build upstanding reputations and attract customers and funding. However, the focus on prohibitions rather than prescriptive requirements may limit differentiation opportunities, and potentially stifle competitive growth.
• The absence of workforce development initiatives in TRAIGA means Texas will need to rely on other programs and private sector initiatives to address AI-related workforce needs. In terms of AI literacy, Texas’s future looks, unfortunately, bleak.
• Critical infrastructure sectors may see varied impacts depending on whether they qualify as governmental entities, with those that do facing stricter requirements around AI disclosure and biometric data use. Non-governmental insurance and healthcare providers may be able to circumvent certain compliance requirements.

Technological

• TRAIGA’s technology-neutral approach to defining AI systems ensures broad applicability but could create complex challenges in determining which systems are covered, potentially affecting everything from rudimentary machine learning algorithms to advanced general-purpose systems and AI agents.
• Deployed AI systems by governmental entities will become more transparent due to mandatory disclosure requirements, though private sector systems may see less direct impact unless they engage in prohibited practices. However, TRAIGA’s private incentive to demonstrate compliance through alignment with reputable standards like NIST or ISO could implicitly favor more robust private sector compliance.
• Support for innovation through regulatory sandboxes will encourage responsible AI innovation and experimentation, but to a lesser extent than expected. Unclear sandbox eligibility and testing criteria could perpetuate an inconsistent, unpredictable, and disorganized AI innovation ecosystem.
• The prohibition on certain uses of biometric data by governmental entities may drive innovation in alternative identification and authentication methods. This circles back to a key weakness: prohibiting certain high-risk AI use cases and not the very practices (e.g., social credit scoring) they enable.
• Anti-discrimination provisions will encourage fairness in AI systems, though the requirement to show intent (not just disparate impact) may limit the practical effect of algorithmic bias mitigation.

Social & Ethical

• Protection from discriminatory AI will be limited to cases where intent can be demonstrated, potentially leaving gaps where AI systems produce discriminatory outcomes through negligence or poor design rather than deliberate intent. Proving intent beyond a reasonable doubt is significantly more difficult than proving discrimination.
• Consumer awareness of AI interactions will increase for governmental services, though private sector AI interactions may remain opaque unless they fall under prohibited categories, which, frankly, aren’t that expansive.
• The establishment of complaint mechanisms through the Attorney General’s office will provide consumers with a formal channel for reporting concerns, though effectiveness will depend on the Attorney General’s resources and responsiveness. We expect substantial early-stage bottlenecks and resource drainage in this context.
• Without explicit workforce development provisions in TRAIGA (which were present in previous versions of the act), addressing the incoming digital divide and rapidly increasing AI literacy needs will require separate initiatives and funding sources.
• Government AI initiatives will be held to higher transparency standards than corporate AI initiatives, potentially creating an imbalance where citizens have more visibility into governmental AI use but limited insight into private sector applications. Public and private AI use should be jointly regulated and held to similar, if not equally intensive, standards.

Long-Term

• TRAIGA’s focus on prohibitions rather than comprehensive risk management frameworks may limit its influence on federal AI regulation, which may favor more nuanced, risk-based approaches; the ability to accurately classify risks in complex AI systems isn’t a straightforward process and requires a high degree of dynamism.
• Texas’s position as an AI governance leader will depend on effective implementation and enforcement, particularly given the act’s reliance on the AG’s office for enforcement and the Council’s limited advisory role. In the future, we wouldn’t be surprised to see the Council receive its own set of enforcement and legislative revision powers.
• TRAIGA may incentivize research on AI ethics, governance, and safety, though without dedicated funding or tangible research provisions, this impact may be limited. However, given TRAIGA’s focus on government AI use, public AI governance, safety, and ethics contracts and collaborations may increase in frequency.
• General-purpose AI models and AI agents are not explicitly addressed, and the broad definition of AI systems may or may not capture their unique characteristics and risks, perpetuating potentially profound regulatory uncertainty as these technologies advance and proliferate.
• Focus on specific prohibited uses rather than holistic risk assessment may leave gaps as new AI capabilities and applications emerge that don’t fit neatly into prohibited categories. There are also many other categories that should be considered prohibited AI use (e.g., psychometric profiling), which TRAIGA does not cover.
• AI governance in Texas will need to evolve through legislative amendments rather than regulatory rulemaking, given the Council’s lack of rulemaking authority; this will likely decelerate adaptation to new AI developments and standards.
• Without explicit sustainability provisions, the environmental impacts of AI systems, particularly large-scale computing infrastructure, will remain unaddressed by TRAIGA. This is emerging as a critical gap across most US-based AI regulations.
• Large-scale data centers, such as those proposed by President Trump’s Project Stargate, which intends to support national AI infrastructure, will operate under TRAIGA’s general provisions but may require additional federal oversight for national security considerations; TRAIGA does not outline any direct considerations for national security risks.

Suggestions and Conclusion

Building on our discussion of TRAIGA’s strengths, weaknesses, and potential impacts, we offer the following suggestions for improving the act:

• Develop a precise and comprehensive tiered risk classification framework that covers all kinds of AI systems that exist today in addition to those we expect will exist within the next decade. As we’ve said before, the current broad AI definition lacks nuance for disparate risk levels.
• Ensure that general-purpose AI systems, AI agents, and AGI are explicitly defined and covered. The goal isn’t to get these definitions “right” but rather, to ensure that working definitions and governance practices exist so that when these systems advance and/or proliferate, a general framework for regulating them can be applied and then adapted accordingly.
• Expand prohibited practices beyond AI-specific implementations to cover the underlying harmful activities themselves, like unchecked surveillance, election manipulation, and social credit scoring. The current approach of prohibiting only AI-enabled versions of these practices creates enforcement gaps and regulatory loopholes.
• Expand the scope of risks covered to include national security, systemic, and existential threats. Once more, the idea here isn’t to capture all possible AI-induced risk trajectories but to have certain foundational provisions in place when we do encounter them.
• Specify, in detail, regulatory sandbox criteria at every stage of participation, from application to exit. Comprehensive criteria will encourage engagement and support consistent and transparent testing and evaluation processes during sandbox participation.
• Consider extending the 60-day cure period for violations to a minimum of 90 days for complex AI system modifications, while maintaining shorter periods for simpler compliance issues. This could reduce the risk that TRAIGA stifles AI innovation.
• Develop concrete and accessible feedback mechanisms, beyond the Attorney General’s complaint mechanism, through which AI companies and citizens can provide direct feedback to the Texas AI Council, and grant the Council more substantive powers beyond its current advisory role.
• Require the Texas AI Council to publicly disclose the logic, information, intent, and individuals involved in AI governance-related decisions, updates, and recommendations to guarantee unobstructed transparency into council practices and appropriately manage its influence.
• Consider expanding Council membership beyond seven members and establishing clear expertise requirements to ensure diverse perspectives are represented in AI governance decisions; there is a reasonable risk that the council will be staffed with industry plants rather than those who are most qualified to oversee AI governance.
• Establish separate workforce development initiatives with clear funding and resource channel priorities to ensure that AI education and training programs are well subsidized. Funding and resource prioritization should be proactive and informed by demographically diverse demand forecasting analyses that look several years into the future.
• Extend disclosure requirements and other key protections to private sector AI use, not just governmental entities, to ensure comprehensive consumer protection and reduce the risk of regulatory capture.
• Address the disparate impact loophole in discrimination provisions by establishing clear standards for when patterns of discriminatory outcomes, regardless of intent, require remediation.
• Create and establish funding and talent channels that are exclusively reserved for AI governance, ethics, and safety research to guarantee that further developments across these fields do not stagnate as AI advancements elevate the uncertainty and complexity of these issues.

We leave readers with these suggestions, though there are surely many more that could be made depending on how far into the future we’re willing to look. Regardless, for those interested in further exploring AI policies, governance, ethics, and safety, we suggest following Lumenova’s blog, where numerous resources examining an expansive array of both present and future-oriented topics are at your disposal.

On the other hand, if you’re already considering or engaged in developing AI governance and risk management frameworks, strategies, and/or policies, we invite you to check out Lumenova’s RAI platform and book a product demo today. For the curious ones, we further invite you to experiment with our AI policy analyzer and risk advisor.

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